11 Jun
info

Evaluating the Facial Esthetic Outcomes of Digital Smile Designs Generated by Artificial Intelligence and Dental Professionals

Abstract

This study evaluates the preference rates for smile designs created by professionals or by Artificial Intelligence (AI) among dentists, dentistry students, and laypeople. Four cases with symmetrical and asymmetrical features were selected based on the Facial Flow (FF) concept from the database of the Smile Designer app regarding anatomical facial points. Two smile designs were created for each selected case: one using Artificial Intelligence (AI) and one created manually. An online survey assessed participants’ preferences for the different smile designs. The chi-square test “Pearson’s and Fisher’s exact test (P)” was used to analyze the survey data. A total of 628 people completed the study. Dentists preferred the manually-created smile design for the first three cases. For Case 4, dentists who used the Smile Designer program preferred the manually-created design (55.88%), while those who did not use the program preferred the AI-generated design (55.84%). There was a significant difference in esthetic perception between dentists and dental students (p = 0.001) and between dentists and laypeople (p = 0.001) for Case 1, only between dentists and dental students (p = 0.003) for Case 2, and only between dentists and laypeople (p = 0.001) for Case 3. Furthermore, we found that females (p = 0.007) and orthodontists (p = 0.025) had a higher preference for the AI-generated design in this case compared to males and other dental specialties for Case 3. While age, education level, and clinical experience did not significantly impact dentists’ preference for manually-created or AI-generated smile designs (p > 0.05), our results suggest that there were some differences in preference for Case 3. Overall, our findings suggest that the use of AI-generated smile designs for symmetric faces is acceptable to both dentists and laypeople and can offer time-saving benefits for clinicians.

Keywords: Artificial Intelligence (AI); dental esthetic; digital smile design (DSD); esthetic perception


1. Introduction:

One of the primary goals of dental treatment is to restore teeth in a way that meets the patient’s needs and creates a natural-looking, esthetically pleasing smile. Owing to technological advancements, esthetic dental materials and treatments are becoming more widely available. The use of three-dimensional (3D) design technology to create personalized, natural-looking, and esthetically pleasing smiles is becoming increasingly popular in dental practices, and “Digital smile design” (DSD) programs are a key tool in this process. These programs allow the dentist and patient to plan treatment and provide a high level of convenience. There are many DSD programs available on the market, and a common feature of all of them is that they evaluate the smile in the context of the entire face. This requires a photograph in which the patient is naturally smiling.

The use of computer technology for the esthetic prosthetic treatment of patients is becoming increasingly common. Studies have shown that digitally-made designs are effective in meeting patients’ esthetic expectations and achieving a predictable, satisfactory treatment outcome. One of the first studies to introduce digital smile analysis and design was conducted in 2002. Initially, programs such as PowerPoint, Keynote, and Photoshop were used for planning smile designs. In the past, clinicians and technicians would perform esthetic analysis on printed photographs or plaster models obtained prior to treatment. These programs allow for the creation of esthetic reference lines on faces and smile photographs on the computer.

In 2008, the first DSD protocol was developed, which associated a series of facial, intraoral, and extraoral photographs with the face. This allowed for a fully digital format transition, enabling the matching of 2D photographs with 3D digital models and the verification and improvement of 3D esthetic parameters. Computer programs and software tools help with the planning, execution, and visualization of restorative, prosthetic, orthodontic, surgical, and multidisciplinary treatments, while enabling the digital planning and visualization of expected esthetic results before treatment. With the increased use of DSD programs in clinical settings, many innovations have been made to make these programs more accessible and practical. One such innovation is the integration of artificial intelligence into the software.

The term “AI” originated in the 1950s and referred to the idea that machines can perform tasks that humans typically perform. In the future, AI is expected to automate esthetic evaluation, smile design, and treatment planning processes. One of the main claims of companies is that smile designs created by AI can be produced in a short period of time (seconds), compared to those traditionally created by physicians.

A key point in designing natural and harmonious smiles is that faces and smiles are not always symmetrical. Various studies have shown a positive relationship between facial symmetry and beauty. Facial Flow (FF) is defined as “the direction in which facial structures travel.” The Facial Flow Line (FFL) can usually be towards the left or right side of the patient’s face. FF is also defined as neutral in cases where the nose and chin tip go in opposite directions of the face, or in cases where the face is symmetrical. Anatomical landmarks on symmetrical faces can be manually determined and used as reference points. However, more research needs to be conducted on whether these landmarks, which are crucial in creating smile designs, can be detected on asymmetrical faces using AI. It is noted that the application of AI in healthcare is still immature and requires significant improvement. AI works according to a specific algorithm, and misinterpretations can occur due to the limitations of those algorithms.

The aim of this study was to evaluate the esthetic perception of facially driven smile designs created manually or using AI. The initial hypothesis was that, in smile design programs, the method of selecting landmarks (manually or using AI) does not affect the esthetic perception of the resulting smile design, regardless of the symmetry of the face.

2. Materials and Methods

2.1. Model Selection

Digital Smile Design Program (Smile Designer App advanced v.1.0, Neuralp Yazılım Bilişim Ltd. Şti., Bursa, Turkey) was used to design smiles. To evaluate the efficiency of the AI mode, the 4 cases were selected based on the Facial Flow (FF) concept from the sample cases in the Smile Designer App’s database. The selection process of the cases was done through a group discussion involving a team of dental professionals with experience in cosmetic dentistry. The cases were chosen based on their clinical relevance and representativeness of the different smile designs examined in the study.

The classification of cases was based on the relationships between reference points on the forehead, nose, and chin tip, which are significant in smile designs. Specifically, the trichion, glabella, subnazale, and menton points were utilized as references to determine the facial flow. By examining the axis formed by connecting these points, we identified four distinct cases: Case 1 represented a facial flow towards the right side, Case 2 represented a facial flow towards the left side, Case 3 represented a scenario where the nose and chin pointed in different directions, and Case 4 represented a symmetrical face (Figure 1). To aid visual clarity, we utilized a color scheme in Figure 1, where the side with the facial flow direction was depicted in green, while the opposite side was shown in red. However, it is important to note that in neutral cases where the nose and chin tips were directed to different sides while maintaining facial symmetry, no green or red sides were indicated.

2.2. Image Manipulation

Using the Smile Designer App program, two different smile designs were created for each case: one in the AI mode and one in the manual mode. The program uses the Microsoft Face API, a powerful AI-based tool that provides facial recognition capabilities. In a Node.js environment, the necessary browser-specific components, such as HTMLImageElement, HTMLCanvasElement, and ImageData, can be polyfilled. This can be achieved by installing the node-canvas package or, alternatively, by constructing tensors from image data and passing them as inputs to the API. The Microsoft Face API combines high-accuracy face recognition with advanced capabilities such as emotion detection, age and gender estimation, facial attribute analysis, real-time face detection and tracking, support for custom recognition models, and seamless cloud integration. These features open up a world of possibilities for diverse applications, ranging from personalized experiences and targeted marketing to augmented reality and virtual try-on experiences. The Microsoft Face API identifies 68 different facial landmarks on a person’s face. These landmarks are crucial for the program’s ability to locate and analyze various facial features, such as the position of the teeth. Once facial recognition is completed, the program calculates the distance between these 68 landmarks. This information is then used to position the teeth accurately. Based on the relationships between the facial landmarks, the program can determine the patient’s facial type (e.g., square, triangle, round). This information is crucial in determining the best possible treatment and approach for the patient. With the information gathered from the facial landmarks, the program can automatically determine the appropriate tooth sizes for the patient, allowing for a more accurate and personalized treatment plan. A single physician with experience in using smile design programs created all of the designs. For each case, the reference points (trichion, glabella, subnasal, menton, pupillary, alare, and chellion regions) were automatically determined through facial analysis in the AI mode. DSDs were then created based on the different tooth shapes and sizes determined by considering the reference points.

In the manual mode, the physician subjectively selected and marked all reference points. Next, the teeth were selected in the AI mode and repositioned according to the reference points determined by the physician, without altering their color, shape, size, or form. The smile design was finally created (Figure 2).

2.3. Preparation of Survey Questions and Administration of the Survey

An online survey (1999–2021, SurveyMonkey, California, CA, USA) was conducted to evaluate whether the manual or AI-based selection of landmarks on symmetrical and asymmetrical faces in smile design programs affects the esthetics of the resulting smile design. The survey link was shared online on social platforms and individuals under the age of 18 were not included in the study. After obtaining consent for voluntary participation in the survey, participants were asked to provide their age, sex, and occupation information. Participants were divided into three subgroups based on their occupation: dentist, dentistry student, and other professionals (laypeople). They were then asked about their professional knowledge and, for each professional group, about their experience and expertise in smile design. If participants had such knowledge and experience, they were asked whether they actively used a smile design program.

For each of the four cases, participants were asked to choose which of two smile designs, one created using AI and one created manually, they found more attractive. Only one option could be chosen in the online survey. Images were included with the options. After the surveys were completed, the data were transferred to a spreadsheet table (Excel 2021; Microsoft Corp., Washington, DC, USA) and analyzed statistically.

2.4. Statistical Analysis

The sample size was calculated using the “G*Power” Software (G*Power 3.1.9.213, Heinrich Heine Universitat Dusseldorf Institute Experimentelle Psychologie, Dusseldorf, Germany). The software output analysis reported 589 as a total sample size, with an under effect size of 0.2, an error probability alpha of 0.05, a power of 99%, and a confidence interval of 95%. After collecting the information from the participants via SurveyMonkey, the data was entered into an excel spreadsheet (Excel 2021; Microsoft Corp). The IBM SPSS Statistics (IBM SPSS Statistics v22; IBM Corp.,Chicago, IL, USA) software package was used for the statistical analysis of the study. The chi-square test “Pearson’s and Fisher’s exact test (P)” was used to evaluate the degree of significance between the categorical variables.

3. Results

A total of 807 people confirmed that they had participated in the study voluntarily, and 628 participants who answered all questions were included in the study. The gender distribution of participants based on their choice between AI-generated and manually-created smile designs is shown in Table 1.

According to the data obtained from the sociodemographic survey questions, 330 dentists participated in the study. It was found that the majority of dentists had 0–4 years of experience (29.87%), while dentists with 25 or more years of experience made up only 4.87% of this group.

The majority of the dentists who participated in the survey did not have a specialty in any field (34.42%). The percentage of participants according to their area of expertise was as follows: prosthodontists (26.95%), orthodontists (10.06%), oral and maxillofacial surgeons (7.79%), endodontists (7.47%), restorative dentistry specialists (4.22%), periodontologists (3.90%), pedodontists (2.92%), and oral and maxillofacial radiologists (2.27%).

It was observed that 47.95% of laypeople and 59.06% of dentistry students who participated in the study stated that they had knowledge about esthetic smile design. The data were collected from a total of 149 students, 103 of whom were female (70.55%) and 46 of whom were male (29.45%). Of the 149 students, 113 had received preclinical education (18.12% in the 1st grade, 30.87% in the 2nd grade, and 26.85% in the 3rd grade), and 36 had practiced on patients (18.79% in the 4th grade and 5.37% in the 5th grade).

For Cases 1, 2, and 3, both dentists who used and did not use a smile design program preferred the manually-created smile design over the AI-generated smile design (preference percentages for Cases 1, 2, and 3 were 82.35%, 73.53%, and 64.71%, respectively, for those who had used a smile design program, and 79.56%, 72.63%, and 53.65%, respectively, for those who had not). For Case 4, dentists who used a smile design program preferred the manually-created smile design (55.88%), while those who did not use a smile design program preferred the AI-generated smile design (55.84%). There was no significant difference between the two groups of dentists for all of the cases (p > 0.05).

The chi-square test was used to evaluate the difference in esthetic preference for AI-generated or manually-created smile designs between different occupational groups. The results showed a significant difference in esthetic perception between the groups for Cases 1, 2, and 3 (p = 0.002, p = 0.025, and p = 0.003, respectively), but not for Case 4 (p = 0.284). A post-hoc test was conducted on the groups where a significant difference was found, and it was determined that there was a significant difference between dentists and dental students (p = 0.001) and between dentists and laypeople (p = 0.001) for Case 1, only between dentists and dental students (p = 0.003) for Case 2, and only between dentists and laypeople (p = 0.001) for Case 3. In general, dentists preferred the manually created smile design in Cases 1, 2, and 3 (79.87%, 72.72%, and 54.87%, respectively) (Figure 3).

When the preference rates for manually and AI-generated smile designs were evaluated according to the area of specialty or status of the dentists (expert or not), it was found that the difference between orthodontists and other specialties was statistically significant only for Case 3 (p = 0.025). Orthodontists generally preferred the AI-generated smile design (70.96%), while dentists in other specialties preferred the manually created design (51.88%) (Figure 4).

When choosing between manually-created and AI-generated smile designs, the age of the individual (divided into ten groups ranging from 18–24 to 65 and above) (Figure 5), their education level (high school/middle school, associate degree, and undergraduate), and the clinical experience of the dentists (0–9 years, 10–19 years, and 20 years or more) were not found to be influential factors (p > 0.05). The chi-square test also showed that gender was not an influential factor in Cases 1, 2, and 4 (p = 0.359, p = 0.464, p = 0.611, respectively). However, for Case 3, a significant difference was observed between males and females, with females preferring AI-generated smile designs over manually-created ones (p = 0.007) (Figure 6).

4. Discussion

Based on the results of this study, the null hypothesis, “the method of selecting landmarks manually or using AI in smile design programs does not affect the esthetic perception of the resulting smile design,” was rejected. The study found significant differences in the esthetic perception of smile designs created manually or by AI.

As digital technology improves and people become more concerned with esthetics, DSD programs have become increasingly popular in dentistry. Accurate diagnosis and treatment planning are crucial in esthetic dentistry, and DSD programs allow for virtual esthetic analysis and treatment planning using edited photographs or digital patient models.

Using 3D digital design can increase patient satisfaction and treatment success. DSD programs provide; however, the ideal features that DSD programs should have are still being researched. A review of commonly used DSD programs found that if one or more esthetic parameters are neglected, the ideal treatment plan and results cannot be achieved. Therefore, it is believed that the selection of landmarks affects treatment planning and the esthetics of the restoration created by the program.

In this study, the landmarks were selected manually by the clinician and automatically by AI. The automatic selection of landmarks by AI saves time, but the fact that dentists generally found the cases with manually selected landmarks to be more esthetically pleasing raises questions about the effectiveness of AI.

For the most accurate determination of symmetry and asymmetry in facial analysis, as many reference points as possible should be used. In the AI mode of the smile design program used in this study, 68 facial points were determined using Microsoft face API.

It was observed in Case 3 that orthodontists preferred the AI-generated smile design at a statistically significant rate compared to other specialties. This may be due to the fact that orthodontists use FM more frequently during treatment, leading to their heightened perception of smiles. The reason orthodontists had different esthetic perceptions in our study may be attributed to this. In a study comparing landmarks marked by orthodontists to those drawn by AI, a high degree of similarity was found. This suggests that orthodontists may have preferred the AI designs in our study due to their familiarity with AI values or experience with treatments based on these familiar landmarks.

In contrast, when examining Case 4 (which featured a symmetrical facial feature), there was no significant difference in preference for manually created or AI-generated designs among dentists and other occupations (p = 0.001). It can be concluded that in complex cases, dentists’ esthetic perception is different from that of people in the non-professional group.

The results of the survey suggest that there is a significant difference in the esthetic perception between dentists and laypeople, similar to findings from another study. This indicates that there may be a discrepancy between the esthetic perceptions of patients and dentists during treatment.

For years, clinicians, researchers, and programmers have favored mathematical approaches for creating symmetrical smiles. However, the FF concept allows for more organic and natural smile designs on both symmetrical and asymmetrical faces. While traditional smile design methods often aim for symmetry by using straight lines, such as the dental midline, as a reference, the FF concept takes into account human perception and may be more suitable for asymmetrical faces. In DSD applications, mathematical algorithms are used to create a new design based on determined landmarks. While AI and DSD programs may produce good results in patients with good facial symmetry, manual methods may be preferred in cases where facial symmetry is impaired. Further research on the use and effectiveness of AI in creating DSDs could help clinicians understand the relationship between landmarks and achieve the ideal smile design, as well as understand human esthetic perception. More studies are needed to determine the best approaches for creating natural and harmonious smiles, and to decide whether facial structures should be altered with a new smile design.

In terms of methodology, this study is the first to evaluate how individuals perceive smile designs created using both AI and manual design in terms of esthetics. While the results of the study provide useful information, there are some limitations to consider. The research was conducted online and the participants were classified into categories based on their occupations. However, due to the random nature of the online survey, it was not possible to include the same number of participants in each group. This means that a more homogeneous and specific distinction of categories may provide more information about an individual’s esthetic perception. Another limitation of the study is that the compatibility of the current “smile design algorithm” used in the smile design program with other 2D and 3D smile design programs is not known.

Future studies should compare the AI mode of different smile design programs to further understand the relationship between AI and manual design in terms of esthetics. Furthermore, future studies should consider comparing the AI modes of different smile design programs to gain a deeper understanding of the relationship between AI and manual design in terms of esthetics. Additionally, we acknowledge that esthetics is subjective and significantly influenced by environmental factors, such as race and culture. Therefore, it is recommended that future research explores the impact of conducting similar studies in various demographic regions to determine how these factors affect esthetic evaluations.

5. Conclusions

Based on the limitations of this observational study, it was found that age, education level, and clinical experience did not significantly affect individuals’ esthetic preferences for smile designs created either manually or by AI. However, when evaluating the esthetic perception of smile designs created manually or by AI, significant differences were observed between genders, professions, and specialties in both symmetrical and asymmetrical situations. In cases where AI-generated smile designs were found to be preferred or comparable to manually-created designs, their use in symmetrical faces could be considered acceptable to both dentists and laypeople, potentially saving time for clinicians.

Author Contributions

Conceptualization, G.C. and G.S.Ö.; Data curation, G.M.; Formal analysis, G.C. and G.M.; Investigation, G.C., G.S.Ö. and F.E.; Methodology, G.C. and G.S.Ö.; Software, S.Ş.; Validation, G.C. and G.M.; Visualization, G.C. and G.S.Ö.; Writing—original draft, G.C., G.S.Ö. and F.E.; Writing—review and editing, G.C., F.E. and S.Ş. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The protocol of this study was approved by the Ethics Committee of Istanbul Medipol University (approval number: 2020/561).

Informed Consent Statement

All participants and cases provided their consent to participate voluntarily in the online survey. In addition, signed consent forms were obtained from the cases whose images were used in the digital smile designs included in the study, in order to allow for the use of their images for open-access publication.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We thank Elif Beycan Şen for helping to create the Digital Smile Designs used in this study.

Conflicts of Interest

The authors declare no conflict of interest.

Source

https://www.mdpi.com/2076-3417/13/15/9001

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07 Jun
info

Assessment of Patient Satisfaction and Treatment Outcomes in Digital Smile Design vs. Conventional Smile Design: A Randomized Controlled Trial

Abstract

Backgrounds

The esthetics of a smile holds significant importance in an individual’s self-esteem and overall quality of life. In the realm of cosmetic dentistry, smile design has traditionally relied on conventional methods, but recent advances in technology have introduced digital smile design (DSD) as a promising alternative.

Keywords: Conventional smile design; digital smile design; patient satisfaction


Materials and Methods:

In this randomized controlled trial, 150 adult patients seeking smile enhancement procedures were enrolled and randomly assigned to one of two groups: the DSD group or the conventional smile design group. The DSD group underwent smile design using digital technology, including intraoral scans, computer-aided design, and 3D simulations. Meanwhile, the conventional smile design group received smile design through traditional methods, involving manual impressions, stone models, and manual wax-ups. Patient satisfaction was measured using a visual analog scale (VAS) ranging from 0 to 100 immediately after the procedure, while treatment outcomes were assessed three months post-procedure by dental professionals using a standardized assessment scale.

Results:

In terms of patient satisfaction, the DSD group demonstrated a mean score of 85.4 (SD ± 6.2), while the conventional smile design group had a mean score of 79.8 (SD ± 7.1). This suggests that patients in the DSD group reported higher levels of satisfaction with their smile enhancements. Regarding treatment outcomes, 92% of patients in the DSD group exhibited excellent restoration fit, occlusion, and esthetics, whereas 78% of patients in the conventional smile design group achieved the same level of excellence. These findings collectively indicate that digital smile design (DSD) may yield superior patient satisfaction and improved treatment outcomes when compared to conventional smile design methods, particularly with regard to esthetic results and overall patient contentment.

Conclusion:

In conclusion, the results of this randomized controlled trial emphasize the potential advantages of integrating digital technology into smile design procedures.

INTRODUCTION

A captivating smile is a vital component of an individual’s self-confidence and emotional well-being, making it a focal point in the realm of cosmetic dentistry. For decades, traditional methods have been employed in the field of smile design to enhance dental esthetics and restore patients’ self-assurance. However, the landscape of dental technology has evolved significantly with the advent of digital smile design (DSD), offering a novel approach to smile enhancement.

Conventional smile design traditionally relied on manual impressions, stone models, and manual wax-ups to visualize and plan cosmetic procedures. While these methods have been effective, DSD harnesses digital technology, including intraoral scans, computer-aided design, and 3D simulations, to create a precise and patient-specific treatment plan. This innovative approach has the potential to revolutionize cosmetic dentistry by enhancing treatment precision and patient satisfaction.

To comprehensively evaluate the impact of DSD, this study embarks on a randomized controlled trial comparing patient satisfaction and treatment outcomes between DSD and conventional smile design methods. By systematically assessing the advantages and limitations of these two approaches, this research aims to provide valuable insights into the future of smile enhancement procedures in cosmetic dentistry.

MATERIALS AND METHODS

Study design

This study employs a randomized controlled trial (RCT) design to compare patient satisfaction and treatment outcomes between digital smile design (DSD) and conventional smile design methods.

Participants

A total of 150 adult patients seeking smile enhancement procedures were recruited for this study. Inclusion criteria encompassed patients with a desire for smile enhancement and eligibility for cosmetic dental procedures.

Randomization

Participants were randomly assigned to one of two groups using computer-generated randomization: the DSD group or the conventional smile design group. Randomization aimed to ensure an unbiased distribution of participants between the two treatment modalities.

Interventions

Digital smile design (DSD) group

Patients in this group underwent smile design using digital technology. Intraoral scans were taken to create digital models of the patient’s dentition. Computer-aided design (CAD) software was employed to plan smile enhancements, incorporating patient-specific preferences. 3D simulations were generated to visualize the proposed changes.

Conventional smile design group

Patients in this group received smile design through traditional methods. Manual impressions were taken to create physical models of the patient’s dentition. Manual wax-ups were performed to plan and visualize the smile enhancements.

Outcome measures

Patient satisfaction

Patient satisfaction was evaluated immediately after the completion of the smile enhancement procedure. A visual analog scale (VAS), ranging from 0 (extremely dissatisfied) to 100 (extremely satisfied), was utilized to measure patient satisfaction.

Treatment outcomes

Treatment outcomes were assessed at a follow-up appointment scheduled three months post-procedure. Dental professionals, blinded to the treatment group, used a standardized assessment scale to evaluate restoration fit, occlusion, and esthetics.

Data collection

Patient satisfaction scores were collected by administering the VAS during a post-procedure visit. Dental professionals assessed treatment outcomes during the three-month follow-up appointment.

Statistical analysis

Descriptive statistics, including means and standard deviations, were computed for patient satisfaction scores in both groups. Inferential statistics, such as t-tests and Chi-square tests, were employed to compare patient satisfaction and treatment outcomes between the DSD and conventional smile design groups.

RESULTS

Table 1 displays the patient satisfaction scores for the two groups. In the Digital Smile Design (DSD) group, the mean patient satisfaction score was 87.2 (±6.5), while in the Conventional Smile Design group, the mean score was 81.5 (±7.2). These scores indicate that patients in the DSD group reported higher levels of satisfaction with their smile enhancement procedures compared to the conventional group.

Table 2 presents the assessment of treatment outcomes in terms of restoration fit, occlusion, and esthetics. In the Digital Smile Design (DSD) group, 92% of patients exhibited excellent outcomes, while 6% were rated as good, and 2% as fair. No patients in this group received a poor rating. In contrast, the Conventional Smile Design group had 78% of patients with excellent outcomes, 15% with good outcomes, 5% with fair outcomes, and 2% with poor outcomes.

These results suggest that patients in the DSD group demonstrated superior treatment outcomes, with a higher proportion achieving excellent restoration fit, occlusion, and esthetics compared to the conventional group.

DISCUSSION

The results of this randomized controlled trial (RCT) provide valuable insights into the comparative effectiveness of digital smile design (DSD) versus conventional smile design methods in the realm of cosmetic dentistry.

The findings from this study reveal a notable difference in patient satisfaction between the two groups. Patients who underwent smile enhancement procedures using DSD reported a significantly higher mean satisfaction score (87.2 ± 6.5) compared to those in the conventional smile design group (81.5 ± 7.2). This difference is clinically significant and aligns with previous research, indicating that digital technology, with its capacity for precise planning and 3D visualization, may lead to improved patient experiences and higher satisfaction levels.

In terms of objective treatment outcomes, the DSD group exhibited remarkable success, with 92% of patients achieving an excellent rating in terms of restoration fit, occlusion, and esthetics. The conventional smile design group also showed positive outcomes, with 78% of patients achieving excellence. However, it is noteworthy that a higher proportion of patients in the DSD group achieved excellent outcomes, indicating the potential superiority of this approach in achieving precise and esthetically pleasing results.

These findings align with the growing body of literature highlighting the advantages of digital technology in dental procedures, particularly in cosmetic dentistry. DSD offers a level of precision and predictability that can contribute to superior treatment outcomes, as demonstrated in this study.

The results of this RCT have practical implications for cosmetic dentistry practice. Dental practitioners may consider adopting digital smile design techniques to enhance patient satisfaction and achieve optimal treatment outcomes. Furthermore, these findings emphasize the importance of ongoing professional development to ensure that dental professionals are proficient in utilizing digital tools effectively.

CONCLUSION

Patients undergoing DSD reported higher levels of satisfaction, and a larger proportion of them achieved excellent restoration fit, occlusion, and esthetic outcomes.

Source

https://journals.lww.com/jpbs/fulltext/2024/16001/assessment_of_patient_satisfaction_and_treatment.193.aspx

References

  1. Robinson FG The impact of a smile. J Am Dent Assoc 2007; 138:1162–6.
  2. Coachman C, Calamita MA, Sesma N Dynamic documentation of the smile and the 2D/3D digital smile design process. Int J Periodontics Restorative Dent 2017; 37:183–93.
  3. Layton D Smile design: Orthodontic perspective. Semin Orthod 2008; 14:146–55.
  4. Coachman C, Paravina RD Digitally enhanced esthetic dentistry – from treatment planning to quality control. J Esthet Restor Dent 2016; 28 Suppl 1 S3–4 doi: 10.1111/jerd.12205.
  5. Gürel G The Science and Art of Porcelain Laminate Veneers. Quintessence Publishing 2003 Available from: https://www.quintessence-publishing.com/deu/en/product/the-science-and-art-of-porcelain-laminate-veneers [Last accessed on 2023 Jan 08].
  6. Ahmed KE, Vaidyanathan M Contemporary Esthetic Dentistry. Wiley-Blackwell 2012 Available from: https://onlinelibrary.wiley.com/doi/10.1111/jopr.13176 [Last accessed on 2023 Jan 08].
  7. Christensen GJ Use of CAD/CAM in the practice of dentistry. J Am Dent Assoc 2010; 141 Suppl 2 3S–6S.
  8. Al-Rawi B, Kaisarly D, Maghaireh GA, Alzoubi KH, Lynch E Influence of training on CAD/CAM restoration quality and productivity: A randomized clinical trial. J Dent 2017; 62:72–7.
18 Mar
info

Dental Restorative Digital Workflow: Digital Smile Design from Aesthetic to Function

Abstract

Breakthroughs in technology have not been possible without influencing the medical sciences. Dentistry and dental materials have been fully involved in the technological and information technology evolution, so much so that they have revolutionized dental techniques. In this study, we want to create the first collection of articles on the use of digital techniques and software, such as Digital Smile Design. The aim is to collect all of the results regarding the use of this software, and to highlight the fields of use. Twenty-four articles have been included in the review, and the latter describes the use of Digital Smile Design and, in particular, the field of use. The study intends to be present which dental fields use “digitization”. Progress in this field is constant, and will be of increasing interest to dentistry by proposing a speed of treatment planning and a reliability of results. The digital workflow allows for rehabilitations that are reliable both from an aesthetic and functional point of view, as demonstrated in the review. From this study, the current field of use of Digital Smile Design techniques in the various branches of medicine and dentistry have emerged, as well as information about its reliability.

Keywords: Digital Smile Design; restorative dentistry; dentistry software; dentistry design


1.Introduction

In recent years, prosthetic and implant-prosthetic rehabilitation in the dental field have undergone a strong development in aesthetics and cosmetics, benefiting from both the improvement of some laboratory techniques and the definition of some anatomical criteria useful to the aesthetics of the smile. One of the most noteworthy innovations in the field of prosthetics is undoubtedly represented by the advent of computer-aided design and computer-aided manufacturing (CAD/CAM) technology, which allows professionals to guarantee repeatable and remarkable results from an anatomical–functional point of view. The accessible costs, the small size of the machines, and the relative ease of use make this method passable, even in small-scale outpatient facilities. New processing software, for example, aims to make the entire rehabilitative work-flow digital, simplifying the professional’s work and also facilitating communication with the patient. A quick search on the main informatics engine indicates all of the software available on the market—among them, an important role is undoubtedly played by the Digital Smile Design method. In this work, we will take into consideration all of the articles in the literature regarding the use of this method. This software allows for excellent communication with our patients on the one hand, but on the other, offers the clinician a tool to make the correct therapeutic choice through algorithms. Rehabilitation follows a digital pathway, and the patient can see the result even before starting. These methods allow for accurate planning and guarantee aesthetic, functional, and predictable results. The software in the medical field, 3D technology, and the bioengineering field have been working in synergy in recent years in order to produce excellent therapeutic tools. The possibility of testing three-dimensional structures virtually, before being able to build or test them on patients, is already a huge step forward; this is possible with finite element analyses, for example, with which you can test useful materials for different dental fields, from fixtures to prosthetics, to the simulation of dental movements. Digital Smile Design allows for a thorough workflow simulating the rehabilitation of a patient, simply starting with appropriately calibrated photos. The facial study is usually done using the reference lines, from which the standardized parameters have been developed for the frontal and profile views of the face. The horizontal reference lines used in the frontal view include the interpupillary and intercommissural lines, which provide an overall sense of harmony and horizontal perspective, present in an aesthetically pleasing face. However, the main limitation of this kind of therapeutic method is related to the several anatomical features involved in the rehabilitation. The treatment for giving an “aesthetic smile” to patients is related to the different anatomical areas involved in the treatments, like the teeth, gingiva, mucosa, lip, skin, and so on, which rely on symmetry, shape, and golden proportions. The purpose of this study is to evaluate the effective use of Digital Smile Design techniques in dentistry and other medical fields. These techniques are used in different medical fields, and we have analyzed and categorized all of these fields, and have evaluated the reliability and predictability of these digital techniques.

2. Material and Methods

2.1. Protocol and Registration

This review is registered at PROSPERO with ID number 122744. PROSPERO is an international database of prospectively registered systematic reviews in health and social care, welfare, public health, education, crime, justice, and international development, where there is a health-related outcome.

2.2. Focus Question

The following focus questions were developed according to the population, intervention, comparison, and outcome (PICO) study design:

  • What are the fields of use of the Digital Smile Design software?
  • Is Digital Smile Design bringing improvements in the comfort of patients and in their treatments?

2.3. Information Sources

The search strategy incorporated examinations of electronic databases, supplemented by hand searches. We searched PubMed, Dentistry, and Oral Sciences Source for relevant studies published in English. A hand search of the reference lists in the articles retrieved was carried out in order to source additional relevant publications and to improve on the sensitivity of the search.

2.4. Search

The keywords used in the search of the selected electronic databases included the following:

  • “Digital Smile Design”

The choice of keywords was intended to collect and to record as much relevant data as possible, without relying on electronic means alone to refine the search results.

2.5. Selection of Studies

Two independent reviewers singularly analyzed the obtaining papers in order to select the inclusion and exclusion criteria as follows. For the stage of reviewing full-text articles, a complete independent dual revision was performed.

2.6. Types of Selected Manuscripts

The review included studies on humans and animal published in English. Letters, editorials, and PhD theses were excluded.

2.7. Types of Studies

The review included all human prospective and retrospective follow-up studies and clinical trials, cohort studies, case-control studies, case series studies, animal studies, and literature reviews published on using Digital Smile Design for rehabilitation and restorative dentistry.

2.8. Inclusion and Exclusion Criteria

The full texts of all of the studies of possible relevance were obtained for assessment against the

following inclusion criteria:

  • Digital Smile Design use for restorative dentistry
  • Advantages of Digital Smile Design.

                 The applied exclusion criteria for the studies were as follows:

  • Studies involving patients with other specific diseases, immunologic disorders, or other oral
  • risk-related systemic conditions
  • Not enough information regarding the selected topic
  • No access to the title and the abstract in English.

2.9. Digital Dentistry

Digital Smile Design

Digital Smile Design is a method that allows us to digitally design the smile of our patients, by obtaining a simulation and pre-visualization of the therapeutic result. Patients are often found by the dentist and are immediately subjected to dental services or therapies, without the dentist himself having planned well or having shared the therapeutic project of a tailor-made smile for the patient with them. On the one hand, Digital Smile Design allows the patient to have awareness from the beginning of the therapeutic plan and for them be the first interpreter in the aesthetic and functional rehabilitation of their mouth, and on the other hand, it allows the specialist to tune in better to the expectations and needs of the patient, in order to pursue their shared goals. These protocols therefore allow for a previsualization of the clinical case and of the therapeutic result, and for presenting the patient, in a clear way, the usefulness of being able to program the rehabilitation and interface clearly with the help of other professional figures. Being able to provide all of the data to the dental technician, or even being able to evaluate the prosthetic–implant–orthodontic rehabilitation is made simpler, by being able to communicate information about the case in a simple and digital way to colleagues.

2.10. Sequential Search Strategy

After the first literature analysis, all of the article titles were screened so as to exclude irrelevant publications, case reports, and non-English publications. Then, researches were not selected based on the data obtained from screening the abstracts. The final stage of screening involved reading the full texts in order to confirm each study’s eligibility, based on the inclusion and exclusion criteria.

2.11. Data Extractiony

The data were independently extracted from the studies in the form of variables, according to the aims and themes of the present review, as listed onwards.

2.12. Data Collections

The data were collected from the included articles, and were arranged in the following fields

(Table 1):

“Author (Year)”—revealed the author and year of publication

“Dental Field”—the dental field of Digital Smile Design was used

2.13. Risk of Bias Assessment

Two authors undertook the assessment of risk of bias during the data extraction process. For the included studies, this was conducted using the Cochrane Collaboration’s two-part tool for assessing the risk of bias. An overall risk of bias was then assigned to each trial, according to Higgins et al.. The levels of bias were classified as follows: low risk, if all of the criteria were met; moderate risk, when only one criterion was missing; high risk, if two or more criteria were missing; and unclear risk, if there were too few details to make a judgement about the certain risk assessment.

3. Results

The results were collected from all of the articles that were taken into consideration. The articles that talk about Digital Smile Design and its use in the field of rehabilitative and restorative dentistry were used. In the article, we have not only taken into consideration the “communicative” utility of the software towards the patients, but also that of therapeutic planning and of aesthetic and functional rehabilitation. The articles included in our review already provide important information regarding the field of use of the current digital techniques. Surely, in the first place, the most common field of use is prosthetic and dental restoration. In second place are the positions that mention digital techniques for periodontal purposes instead. Later, we will review these works more closely. Although these techniques are modern and relatively new, the purpose of this work is not to indicate whether these techniques are reliable or not, because the available data available are still few. The aim is to highlight the use-trend in different dental fields.

3.1. Study Selection

The article review and data extraction were performed according to the Preferred Reporting Items for Systematic Reviews and Metanalyses PRISMA flow diagram (Figure 1). The initial electronic and hand searches retrieved 26 articles. After the titles and abstracts were reviewed, only 24 articles were included.

3.2. Study Characteristics

During the selection of the studies, their individual characteristicswere evaluated. The characteristics assessed mainly concerned the field of use of Digital Smile Design (Table 1), as follows:

  • Restorative dentistry
  • Periodontal surgery
  • Implantology
  • Guided bone regeneration
  • Orthodontics
  • Maxillofacial surgery

3.3. Risk of Bias within Studies

Summarizing the risk of bias for each study, most of the studies were classified as unclear risk. More studies were considered as having a low risk of bias.

3.4. Risk of Bias across Studies

There were several limitations present in the current review. The current review includes studies written in English only, which could introduce a publication bias. There were various degrees of heterogeneity in each study design, case selection, and treatment provided among the studies.

4. Discussion

Today, dental care tends to be more conservative than in the past, above all thanks to the advances in medicine. In this section, we want to examine more closely the results obtained by the individual articles evaluated. According to Santos et al., the use of dental planning software can also be used for periodontal surgery. In their article, they considered a case of periodontal plastic surgery appropriately programmed through the Digital Smile Design. Digital Smile Design (DSD) allows for a complete planning of the treatment; the programmed results and those obtained after the surgery are comparable.

The increase in the clinical crown is an intervention that must always be appropriately planned. Patients accept better oral surgical techniques if techniques such as DSD are used. In another study, Meereis et al. considered Digital Smile Design for aesthetic rehabilitation, and the usefulness of this tool is again confirmed. In this work, it is considered with the combined approach of gingival plastic surgery and restorative dentistry. In this case, the patient’s rehabilitation is performed with lithium disilicate glass ceramic veneers. In the work of Cattoni et al., prosthetic planning is carried out with a digital workflow. The limit of these technologies, according to the authors, is represented by a two-dimensional (2D) workflow—the technique faced by the authors in this case provides a totally digital CAD/CAM process to minimize errors. The three-dimensional (3D) planning is sent to the dental laboratory for the fabrication of the prosthetic products. The technique of combining the Digital Smile Design digital workflow with the .stl files from the digital optical impression allows for the realization of these artefacts in the laboratory. A study by Omar and Duarte published in the Saudi Dentistry journal in 2018 reviewed various programs used for Digital Smile Design, and therefore for treatment planning. In this study, different programs were evaluated (Photoshop CS6, Keynote, Planmeca Romexis Smile Design, CEREC SW 4.2, Aesthetic Digital Smile Design, Smile Designer Pro, DSD App, and VisagiSMile). The authors evaluated the reliability of the latter, although some of the programs were not designed for the dental field. The authors in fact focus on this. The possibility of having functions concerning oral structures, such as teeth or gums, makes the work much quicker and more predictable. In 2015, Arias et al. carried out a further study on the approach using Digital Smile, to perform and plan a periodontal surgery in order to solve a gummy smile. Perez-Davidi carried out a study on prosthetic rehabilitations in monolithic material, with CEREC CAD/CAM systems. There is the possibility, therefore, to perform an immediate mock up and then combine the data from Digital Smile Design and CEREC SW4 for manufacturing.

The approach to improve patient aesthetics through Digital Smile Design techniques is further confirmed by other works, such as that by Tak On et al. . The authors confirm the possibility of planning the prosthetic treatment in a preventive manner. Some authors, such as Daher et al., have evaluated the possibility of using cheaper strategies to perform an analysis of Digital Smile Design, such as obtaining images with mobile phones. Trushkowsky et al.  confirm the possibility of carrying out aesthetic evaluations for oral rehabilitations. According to Garcia et al., the use of new digital tools offers important perspectives for the daily clinic; in his study, a prosthetic rehabilitation of the anterior maxillary area is evaluated, all planned through Digital Smile Design. According to the authors, in addition to offering a powerful tool to propose treatment plans to patients, by showing them, it is also useful for planning. Coachman et al. also evaluated the use of this software for the planning of total rehabilitations. In this study, the software was used to plan a computer-guided surgery, and a computer-aided design and computer-aided manufacturing (CAD/CAM) of the final prosthetic devices. In this case, therefore, Digital Smile Design was useful for the implant-prosthetic rehabilitation. According to a study by McLaren et al., cosmetic and aesthetic dentistry have undergone a new push from these digital tools. The authors also show how software like Adobe Photoshop can highlight alterations to the smile and can be useful in the analysis of the patient . In 2008, Stanley et al. published an article about digital workflow. The patient of his work, suffering from a Temporo-Mandibular Joint TMJ disorder, underwent a prosthetic rehabilitation with veneers and crowns, with a minimally invasive approach, in order to rehabilitate the loss of vertical dimension and to resolve his joint pains. The protocol was completely digital, using Digital Smile Design for planning and the CAD/CAM techniques for production. An article in the Journal of Prosthetic Dentistry speaks about the possibility of performing a total rehabilitation supported by implants and a guided bone rehabilitation based on Digital Smile Design.

The patient suffering from a serious bone deficiency, after a digital planning, therefore, is subjected to a vertical and horizontal bone augmentation. For this reason, surgical templates have been built, and then the fixtures are positioned in order to allow for implant-prosthetic rehabilitation. The final rehabilitation involves a total prosthesis screwed on the implants. Pinzan-Vercelino et al. in their article talk about a multidisciplinary approach using Digital Smile Design for aesthetic rehabilitations in patients with medial diastema of the maxilla. Marsango et al., in Oral and Implantology, talk about the digital workflow. The aesthetic planning and simulation of the dental treatment were carried out on two arches, and subsequently, the scans were sent to a laboratory for production using CAD/CAM techniques. In this case, a prosthetic implant prosthetic rehabilitation was evaluated. A further work taken into consideration sees the use of Digital Smile Design for different dental fields. These techniques, according to the authors, can be used for periodontology, implantology, and prosthetics. Thanks to this software, it is also possible to program pre-prosthetic surgery in detail. Veneziani M. proposed a study on this approach using Digital Smile Design for the treatment of complex cases. In this article, he evaluated the possibility of rehabilitation with veneers, including different branches of dentistry such as periodontal therapy, mucogingival, restorative dentistry, orthodontics, and prosthetics. Therefore, a multidisciplinary approach for the patient thanks to this digital protocol using still porcelain veneers was found. Another interesting article talks about the possibility of rehabilitating a second-class brachyphosis patient with a mandibular asymmetry.

This complex case involves planning with Digital Smile Design. The multidisciplinary approach in this case includes periodontal, oral, orthodontic, prosthetic, and maxillofacial surgery. The surgical treatment consisted of an osteotomy of the bilateral mandible and a genoplasty; after the surgery, the plates were screwed, and finally removed. Orthodontic treatment followed the surgery and the prosthetic rehabilitation was scheduled. Elaine Halley spoke about the future of dentistry and 3D planning in changing the facial appearance of patients. The latest article to be taken into consideration in this review, in addition to evaluating the use of Digital Smile Design in aesthetic dentistry, sees a case of rehabilitation of the anterior jaw, in this case using metal-free ceramic crowns, passing first for the provisional function of conditioning the gingival tissues. In this work, we therefore broadly considered the use of a digital workflow in order to

allow for an oral rehabilitation in the different fields of dentistry. Certainly, this technique, already mentioned by some authors, is also useful for correcting important bone defects after invasive surgeries in the case of new cancers. Digital techniques can program correct alveolar preservation after extractions with the different techniques present. Soft tissue management and proper planning allow for gingival health to be maintained in our patients. In addition, minimally invasive rehabilitations that can be designed allow for the maintenance of dental tissues, while ensuring a correct interface between the dental and prosthetic surfaces, so that there may be correct adhesion.

Surely, these types of techniques will progressively tend to replace all of the analog techniques, such as impression techniques, which have different disadvantages. Indeed, with the possibility of being able to provide rehabilitation, it will be possible to make this more predictable. The ideal situation

would be to have the advantages from the digital evolution in all of the fields of dentistry—imagine the possibility of knowing precisely the margins of a prosthesis, or even the root canal anatomy, for prosthetic rehabilitation, all the way through to the pins. In this way, it would be possible to predict the angulation and orientation of these beforehand, so as to program the prosthesis, or, for example, by knowing the canal anatomy , to know how our instruments will behave during the different therapies. The possibility, in fact, to be able to predict the behavior of the tools that are used by the clinician, would be a very useful target, especially in the aforementioned therapies, where the tools can affect a result if they go against fracture, or experience breakage or wear during treatment. Some materials also suffer, as a result of mechanical fatigue, or even physical or chemical treatments. The latter can also occur within the oral cavity itself. With the improvements in the software over the next few years, it will be possible to program the rehabilitation of a patient by combining the files coming from a CT scan or a Cone Beam, along with the .stl files of an oral impression or a facial scan and a photo. All of this guarantees the rehabilitation desired by the patients as well as guaranteeing their satisfaction. Combining all of this with the predictable wear and tear of different materials, would definitely make rehabilitation more reliable. At this stage, this is the main limitation. Several treatment opportunities, in the field of oral surgery and prosthodontics, as well as dental materials, are available today. Future perspective studies should be directed to managing those different anatomical areas related to the different disciplines. However, the results of the present study still underline how, even if there is significant progress in the field of computer-assisted medicine and dentistry, the clinical evaluation of the patients during the first visit, and therefore the close cooperation between the oral surgeons, radiologists, and prosthodontics, cannot be replaced without compromising the final long term aesthetic and functional results of the patients involved in the treatment. Further clinical studies will help to improve on the management of difficult cases.

Surely, this analysis of the individual articles included in the review has brought to light other evidence regarding the use of digital techniques for dental or medical planning. The fields of use have been clarified, how these techniques are used and what reliability they possess have also been clarified, although it is not possible to obtain statistical results as a result of a lack of data.

Limitations

This work takes into consideration the fields of use of Digital Smile Design, so it does not compare the statistical data from the individual studies. The low number of studies in the literature for this topic unfortunately represent a disadvantage. This is a very current topic and is still not widely dealt with in the scientific field, and our study clearly explains what the fields of use are in dentistry for using this digital instrument, so it is anticipated to have good scientific confirmation. Having a large number of scientific articles available on this topic that contain detailed information on the reliability, accuracy, and predictability of these methods, would certainly be a good starting point for further review.

5. Conclusions

In could be concluded from all of the articles present in the literature regarding Digital Smile Design, that this tool provides important information to the clinician and patient. Patients can view their rehabilitations even before they start, and this can have important medico-legal functions. In recent years, these digital techniques have undergone a great positive evolution. It is also possible to remember that other techniques, such as engineering finite element analysis, have provided great support to the biomedical field, allowing for the simulation of structures even before being tested on patients, improving the quality of the rehabilitations and the predictability of the latter. With regard to planning, digital instruments appropriately interfaced with other digital files concerning radiographs and dental laboratory machines thus allow for rehabilitations that are more predictable. Indeed, technology has been evolving in this field in recent years, and will continue to include big updates on Digital Smile Design. However, facial scans would be able to make predictions of bone growth in children, to plan orthodontic–orthopedic rehabilitations, and then drive the proper growth of the jaws.

Source

https://pubmed.ncbi.nlm.nih.gov/39585006/

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18 Feb
info

Digital Smile Design

Abstract

One of the important ways of social interaction is through verbal and nonverbal communication. The human face is capable of eliciting multi‑response according to the situation; amongst them, a smile plays a significant effect in relaying a positive communication that can immensely influence societal outcomes. An important part of dental treatment is to restore the tooth to functionality and to esthetically rehabilitate it, which forms the core of esthetic dentistry. Modern advancements have led us to various esthetic treatment options. Recently, due to the boom of information technology, we are progressing into the digital age where everything has almost been made through computers and the internet. In the dental field, advanced software is being used to modify and create smiles, thereby completely revolutionizing esthetic dentistry. Digital smile design is a recently introduced concept and software which analyzes the smile of an individual through various input scanners and photographs. They provide a plethora of solutions and predictions as to how the smile can be designed, to the point it can pinpoint minor corrections. Here, we discuss the importance of smiles and the analysis using digital smile design.

Keywords: Aesthetics, dentistry, digital smile design, smile, smile design


Introduction

Esthetic dentistry has become one of the most sought‑after disciplines in dentistry which focuses on the smile and pleasing appearance. Modern dentistry is not limited to just the repair of individual teeth. There has been an increase in the incidence of patients who give esthetic outcomes the main priority with the restoration of the tooth structure. Technological advancements have revolutionized restorative dentistry with the introduction of silicate cement, acrylic, and composite resins. Procedures such as bleaching, bonding, and veneering, all not only repair the tooth but also create an aesthetically pleasing smile.

A healthy and attractive smile represents an individual’s spectrum of feelings and emotions in a positive way. This depends on the arrangement of their teeth and soft tissue structures. An attractive smile is indicative of a high societal feeling and influences their self‑confidence, thereby boosting their personality.

An esthetic makeover or smile design involves creating a smile where the stomatognathic structures function without hindrance to each other, a perfectly functioning orofacial structure compliments each other. Designing an esthetic smile is very essential in formulating an esthetic makeover.

Historical Perspective of Esthetics

The concept of esthetics dates back to 900 BC when tusks of animals were carved to form esthetic structures that were used as ornaments and relics. They are derived from the Greek word aisthetikos which roughly translates to “sensitive, sentient, pertaining to sense perception.”

It was the Greek philosophers who during 490–265 B.C.

described the factual existence of the golden ratio observed in nature and subjected to the interpretation of the human mind. The term “golden ratio” was coined by Euclid, the famous Greek mathematician. Esthetics dental restoration was given priority by upper‑class citizens and royals during the second Reich in Rome around 31 B.C. to 476 A.D. Many oral hygiene products such as toothpaste and mouthwashes were used extensively by women in Rome in order to intensify their beauty rather than hygiene in mind. When a tooth was lost, the Romans often resorted to replacing it with an ivory tusk that had been carved to resemble the tooth.

An early version of the veneers was implemented by the Mayans in 1000 A.D, where ornamental fillings were restored on the incisal edges of the teeth; some of the materials that were commonly placed include iron pyrites, obsidian, and jade. Since then, evolution in esthetic dentistry has plateaued. It was not until Pierre Fauchard sparked the interest in esthetics by designing gold‑facing crowns on enamel surfaces and labeled the filling material as incorruptible. This was followed by the invention of the first direct tooth‑colored filling materials using porcelain by M. Richmond and M. Logan in the 1880s. Although they were highly esthetic, they had poor mechanical properties and really showed a perfect fit and so in the 1890s veneers made from porcelain were invented which were fixed onto the tooth structure using zinc phosphate cement.

It was not until the late 1960s that Michael Buonocore introduced enamel etching and bonding, which had significantly increased the bonding strength of the restoration. The subsequent development rapidly led to their high use in restoring the tooth. In recent years, there has been a surplus of product development and innovations that dentists can resort to based on the patient’s requirements. Intraoral scanning, 3D scanning, and highly advanced photoshop labs are employed in predicting treatment outcomes preoperatively. Through this, the patient has an idea about what his expectations would look like and how outcomes suggested by the dentists would look.

Smile

A smile occurs as synchronous coordination that involves contraction and relaxation of several muscles of the face, namely, the depressor anguli oris, therisorius, zygomaticus major and minor, and levator labii superioris. Some of the muscles form a confluent around lip commissures called orbicularis oris and are supplied by the facial nerve.

Humans have two types of smiles: a natural smile, which occurs naturally and is brought about by the maximal contraction of the upper and lower lip elevator and depressor oris; the other type is the social smile, which is voluntary and meant as a kind gesture directed toward another individual. In the facial region, different zones were designated by Ackermann et al. A display zone is formed by the upper and lower lips within which the teeth and the gingival apparatus are seen. The soft‑tissue components of the zone include the thickness of the lip, inter‑commissural width, inter‑labial gap, smile index (width/height), and gingival architecture. The curve formed by the incisal edges of the maxillary anterior teeth forms the “smile arc”. Smile style is the result of contraction and relaxation on muscles in the zone.

Principles of Smile Designing

Designing a perfect smile using the software requires a thorough knowledge about the muscles and dimensions of the display zone along with their esthetic proportions. Since not all individuals are alike, each person should evaluate and study with accurate detail in order to come up with their perfect smile. All these elements are connected with each other. Any change will definitely affect the other element. Although a software algorithm is used in predicting a perfect smile, clinical smile designing requires a multidisciplinary intervention that includes branches of dentistry such as orthodontics, orthognathic surgery, periodontal therapy, and plastic surgery. Facial features that are key in planning for esthetic smile redesign include facial symmetry, facial profile, and proportion of the facial structures. According to literature, an ideal facial feature should have the distance between two superciliary arches equal to the total width of the face (from one zygomatic prominence to the other). The intercanthal line or the pupillary line should be perpendicular to the Frankfurt’s horizontal occlusal plane. Considering the normal verticalis part of the face, three imaginary lines drawn should divide the face into three parts: the glabella to the superciliary arch, from the arch to the tip of the nose, and from the subnasale to the mention of the chin. An ideal smile should have the foundation of an ideal lip. When smiling, around 2 mm of the maxillary incisors along with the interdental papilla should be visible; too much exposure reveals the gingiva, resulting in a gummy smile, while too little exposure flattens the philtrum of the upper lip and produces a frowned appearance.

Dental Composition

As previously discussed, the proportion of the maxillary incisor display holds key to the perfect smile. It has been deliberated to such an extent that even mathematical proportions have been formulated. The width: length ratio of the maxillary central incisors was estimated at 4:5 mm with a width range of 0.8–1.0 and length range of 75%–80% width being most acceptable. The morphology of the incisors teeth their incisal edges and the amount of canine exposure also play a crucial role in smiling. Some of the mathematical theories that were established in predicting the correct proportion include the golden proportion (Lombardi), recurring esthetic dental proportions (Ward), M proportions (Méthot), and Chu’s esthetic gauges. Some of the other dental landmark points that influence the smile include the midline of the dentition, crown length of all incisors and canines, zenith points, axial inclinations, interdental papilla exposure, and contacts.

Evolution of smile design

Before the invention of photoshop and advanced photo tracers, perfect smile lines were drawn by hand and then printed over the photos of the patient and would often be discussed with patients for their input. This process has now been largely replaced by smile automation software, referred to as Digital Smile Design (DSD) software, all of which with the click of a button tells us the modifications needed to be executed in order to get a perfect smile. Some of the key milestones in the evolution of smile design include: Generation 1. Manual hand drawings using pencil markers were made over the patient’s full profile photographs. The drawback of this method was that if taken with a study model, the correlation between the patients full profile photo and study model was very poor. Generation 2. With the creation of Microsoft Office, drawings were often done digitally and then correlated with the model. This helped in tracing minor modifications that needed to be done. Diagrams were often 99% accurate.

Generation 3. The next generation enabled the two‑dimensional (2D) drawings to be linked with physical analog models which permitted a wax‑up of the final smile. Generation 4. The 2D drawings were written up into an algorithm that was then processed digitally and this step enabled the technique of facial 3D analysis, also determining the facial components and aesthetic parameters. Generation 5. Innovation of intraoral camera which allowed us to scan and take digital impressions which were more accurate than impressions taken with any

other conventional method.

Generation 6. Introduction of 4D where digital sensors placed on the patient’s jaw captures the smile motion and movement inside the 3D environment using the MODJAW software and designing the smile with CAD/CAM technology. This technology reduces the need for changes, including reduced tooth preparation and other issues by testing the design with the real movement of the jaw.

Smile Analysis

Smile design has become one of the main purposes of orthodontic treatment. This analysis enables the dentist to appreciate the ups and downs in the facial and dental components of patients’ smiles. Prediction of whether orthodontic intervention is required or not depends on the malocclusion type. If implemented, whether it would be useful or not can be assessed using smile design.

Digital Smile Design

Digitalization has now assumed an important aspect not only in engineering but also in the dental field. Digital Smile Design (DSD) is a modern versatile innovative dental treatment planning tool invented by the Brazilian dentist Christian Coachman in 2007 that permits the professional in digitally design the smile of the patient from a series of pre‑ and post‑DSD photographs. DSD software also allows the clinician to educate the patients regarding the improvements that can be done and also helps in collecting the patient’s own preferences and requirements, thereby making the patient feel like he is a part of the decision‑making process rather than just being on the receiving end. DSD was described by its developers Christian Coachman and Marcelo Calamita as an innovative multipurpose analytical software that is capable of diagnosis, performing meticulous analysis of the patient’s facial and dental traits that may have eluded conventional photography and the human eye.

Requirements of digital smile design

Some of the software that can be used for digital smile design include Photoshop (Adobe), Microsoft PowerPoint (Microsoft Office, Microsoft), Smile Designer Pro (SDP) (Tasty Tech Ltd), Aesthetic Digital Smile Design (ADSD ‑ Dr. Valerio Bini), DSD App by Coachman (DSDApp LLC), Keynote (iWork, Apple, Cupertino, California, USA), NemoDSD (3D) and Exocad DentalCAD, a digital SLR camera. A digital impression of both the jaws is taken with a digital intraoral scanner. The impressions are then uploaded to the CAD/CAM processing machine where they are 3D‑printed. High‑resolution full profile photographs representing the facial profile and frontal views of the patient are essential and videos that record the dynamic changes of the teeth, gingiva, lips, and facial muscles brought about by smiling and talking are essential as this documentation forms the blueprint on which smile design is executed. Three basic photographic views are fundamental in smile design, they include

  1. Full facial view with a natural smile.
  2. Resting face.
  3. A view representing the maxillary and mandibular arch not in occlusion

A magnification of a 1:1 view picture of the central incisor with a black background provides in‑depth detail for the lab technician to work on.[20] The videographic demonstration containing documentation is imported into the slide presentation. The facial and dental components of the smile and their points previously discussed above influence the majority of smile design. Commercially available DSD software include; CEREC Smile Design (SIRONA), Digital Smile System (DSS), Smile Design Pro (TASTY TECH), G Design (HACK DENTAL), Romexis Smile Design (PLANMECA), and Smile Composer (3 SHAPE).

DSD Workflow

The DSD workflow begins with digital scanning of the patient’s dentition using an intraoral scanner, which is then imported to the respective DSD software. Using the various different shapes and forms available in the digital repository, we can overlap the teeth for a given esthetic procedure. The DSD workflow then proceeds as follows:

  1. After uploading the facial photographs, two baselines are drawn on the center of the slide so that it forms a + sign, in a way that it appears to be placed between the upper and lower anterior with the teeth apart [Figure 1]. Horizontal reference lines are achieved through the interpupillary line creating a digital facebow.
  2. Soft tissue features (gingiva, lips, facial lines) and their association with other components are evaluated by grouping and transferring them to the facial photograph.
  3. A template tooth that is set to be standard and exact in dimensions is placed over the original photograph so that axial inclinations, proportion in relation to adjacent teeth, and soft tissue silhouette are established. The retracted view is engaged in order to evaluate whether the intraoral photograph is concurrent with facial baseline data, where three lines are drawn [Figure 2]:

Line 1: Intercanine width measured from the tip.

Line 2: The middle third of the central incisor to the occlusal edge of the adjacent central incisor.

Line 3: From the philtrum of the upper lip to the interdental papilla and the incisal embrasure.

  1. Rectangular crop mode is then chosen and placed over the region of both central incisors to measure the width/length proportion of the central incisors [Figure 3].
  2. Using editing tools, the template tooth can be placed over the photographed tooth, and pasted and morphed according to the best esthetic outcome. The patient’s preferences and inputs can also be gathered and included during this step [Figure 4].
  3. A digital ruler available in the software can be used to calibrate the real‑time dimensions of the tooth by recording the measurement on the 3D model and then incorporating it into the software. Gingival contour and the proportion to attached gingiva width and incisal edges can also be calibrated [Figure 5].
  4. Transferring the + sign to the cast: The measurement of baseline point till the free gingival margin is recorded and then transferred to the 3D cast with the aid of a caliper. Horizontal lines above the teeth which predict the gingival margin are marked on the cast using a pencil mark. The vertical lines are then marked using the interval between the incisal embrasures along with the facial component, which is then marked in the 3D model [Figure 6].
  5. Wax up of the procedure to be performed for establishing a smile and then carried out on the cast and evaluated using DSD, after which it is tried on the patient.
  1. Once the approval of the wax‑up has been sought, minor corrections, if deemed necessary, are performed.
  2. Minimal intervention should be prioritized such as minimal reduction of tooth surfaces, and giving proper clearance for crowns, if required [Figure 7].
  3. Attention to detail in each step in DSD usually results in an outcome that goes beyond the patient’s expectations.

Advantages of DSD

DSD enables the patient to actively participate in their treatment plan, resulting in much higher compliance and better motivation, as the results are evident from the 3D previsualization and simulation. The changes can be customized according to their desires. Digital scanning allows the clinician to detect any insidious disease due to the relatively high shades of gray (256 pixels) compared to a conventional radiograph (16–25 pixels). DSD shields the patient from any unnecessary radiation exposure due to the digital PSP sensors compared to the solid‑state sensors. Digital imaging also saves on essential sources. A study conducted by Cervino G et al.  in his review stated that DSD gives valuable feedback which can be discussed and improved upon. It drastically improves the communicative link between the patient, the clinician, and the technician in treatment planning to improve the smile line and facial features when smiling. This also gives the clinicians the comfort of avoiding any medico‑legal issues as the approval of the patient pertaining to postvisualization photographs is obtained before the treatment is implemented.

Disadvantages of DSD

Although DSD presents with an attractive treatment planning tool for the patients, it has certain limitations. It poses as an expensive set‑up as the costs for purchase and repair are considerably high. It cannot be operated by any person; rigorous training is required to learn the tool. Sometimes, the patient disagrees with the pertaining outcome of the treatment even though the software had predicted a better outcome. In such cases, the software blame might seem illogical. This scenario has already been advertised by the manufacturers which often state “the enhanced image does not always match the original image”. It is important to watermark the clinician’s respective work so as to eliminate unauthorized reproduction of the images. It is recommended that copies of the original images be stored on the computer or network server.

Conclusion

The DSD is an innovative tool that helps the clinician to create esthetically pleasing smiles. Previsualization drastically increases the patient’s acceptance rate. The technology also makes the patient a part of the decision‑making process by including their preferences. Although caution should be exercised that ideal case selection is always necessary in order to have a successful outcome. Patients should be enlightened about the potential ups and downs that they might face if the results are not up to their expectations. Further research into this area will definitely address and solve the issue and make this technology central to esthetic dentistry.

Source

https://pubmed.ncbi.nlm.nih.gov/39585006/

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18 Jan
info

Artificial Intelligence in Aesthetic Dentistry: Is Treatment with Aligners Clinically Realistic?

Abstract

Smile aesthetics are increasingly prioritized in dental practice, with accurate orthodontic assessment and treatment planning being crucial for optimal outcomes. This study evaluates InvisalignR SmileView™ (SV), an AI-based tool that simulates post-treatment smiles, focusing on its ability to present potential orthodontic outcomes to patients. Background/Objectives: This research aims to study whether SV can simulate predictable orthodontic results and if it makes anatomical modifications to the teeth. Additionally, it will evaluate whether SV displays smiles that conform to the orthodontic criteria described in the literature. Finally, the study will analyze whether the software can align the dental with the facial midline. Methods: A total of 51 subjects were recruited in Madrid, Spain. The operator took a frontal photograph of the subjects with a social smile (T0), following the application’s instructions. Subsequently, the subjects followed the steps to modify their smile (T1), resulting in a new image of the subject with a different smile. The following variables were collected, analyzed, and compared with the standards defined in the literature: smile width, vertical exposure of the maxillary central incisor, width of the maxillary central and lateral incisors, proportion of the maxillary lateral incisor width to the central incisor, anterior gingival exposure level, position of the upper and lower dental midlines relative to the facial midline. Results: 58% of the sample showed dental expansion, with an excessive expansion (>5 mm) observed in 8%. In the maxillary arch, 5.9% of incisors exceeded predictable aligner movement (>1.5 mm), with 3.9% showing excessive extrusion and 2% excessive intrusion. For the lateral incisors, the mesiodistal size was reduced less than 0.5 mm in 31.4% of cases, with excessive interproximal reduction (>0.5 mm) in 5.9%. Additionally, 62.7% of cases would require multidisciplinary treatment due to an increase in size. SV centered the upper midline in 77.9% of these cases. Among the sample, the upper midline was initially centered in 74.5% of subjects, and SV maintained it centered in 84.2% of these subjects. Conclusions: SV tends to generate simulations of broader smiles, which are mostly achievable through aligner treatments, from an orthodontic perspective, and showed high predictability regarding the vertical movements of the incisors that can be achieved with aligners. Moreover, it adjusted the mesiodistal size of the upper incisors in its simulations and demonstrated the ability to identify and correct deviations of the dental midlines relative to the facial midline.

Keywords: SmileView™; artificial intelligence; smile simulation


1. Introduction

Smile aesthetics play a crucial role in contemporary dental practice, as reflected by the increasing demand for more cosmetic and aesthetic procedures from patients. Achieving an optimal aesthetic outcome in oral rehabilitation requires several critical stages, including a preliminary orthodontic assessment, accurate diagnosis, and appropriate treatment planning. These steps are essential in the overall process of dental and facial restoration. From an orthodontic perspective, the smile comprises various features, such as the smile line, which is defined as the distance between the upper lip and the maxillary anterior teeth when smiling, and the smile arc, which is the relationship between the curvature of the maxillary anterior teeth and the upper border of the lower lip.

The number of teeth displayed (the count of visible teeth when smiling), the size of the buccal corridors, the variations between the central and lateral incisal edges of the maxillary incisors, the presence of crowding or diastemas, the overbite, and the relationship between the dental and facial midlines all play a fundamental role in the aesthetics of the smile. Additionally, aspects such as excessive gingival display when smiling, known as a “gummy smile”, which is not a pathological condition and occurs when more than 3 to 4 mm of gingiva are visible when smiling, must also be considered.

There are two main types of smiles: the social smile and the emotional smile. The social smile is deliberately used in social situations to express politeness and kindness, adhering to social conventions. In contrast, the emotional smile is genuine and reflects authentic emotions such as joy and affection. Both types are crucial in our communication and interaction with others.

InvisalignR SmileView™(SV) is an interactive tool provided by InvisalignR that allows prospective patients to visualize how their smile might improve following treatment with aligners. This technology employs 3D modeling to display the patient’s current smile and simulate the changes that Invisalign aligners can achieve in dental alignment. This way, patients can virtually see potential outcomes before initiating the actual treatment. It is important to note that the smile simulator is based on a vast database of 6 million Invisalign cases, offering a precise simulation of possible dental results. This system uses a machine learning algorithm to achieve this accuracy. The official launch of SV was in 2019. Dr. Rhona Eskander, at the British Dental Conference and Dentistry Show in 2019, mentioned that the primary purpose of SV is to attract patients, thereby increasing treatment acceptance and driving business growth.

Regarding the creation of that new smile, there is no information available on the specific orthodontic movements performed by artificial intelligence (AI) to alter the smile, nor is it known if they are biomechanically feasible. To date, no studies have been found that analyze the aesthetic smile” standards defined by AI or confirm whether it meets the clinical criteria established by the scientific community. Therefore, this research aims to study whether SV can simulate predictable orthodontic results and determine if it makes anatomical modifications to the teeth. Additionally, it will evaluate whether SV displays smiles that conform to the orthodontic criteria described in the literature, including gingival display. Finally, the study will analyze whether the software can align the dental midline with the facial midline.

2. Methods

2.1. Study Type and Design

A prospective longitudinal cohort study was conducted in two phases: T0 (measurement of smile characteristics in an initial photograph) and T1 (after modifying the smile using the SV application).

2.2. Study Population

A power analysis was performed to determine the minimum sample size required for a test with two variables, with a significance level of α ≤ 0.05 and a power of 80%. Based on previous studies, it was established that a 2 mm difference in incisor display would be clinically relevant, and a 3 mm difference in smile width would be statistically significant. The minimum sample size was determined to be 48 subjects. A total of 51 subjects were recruited in Madrid, Spain, from shopping centers, educational institutions, and offices. Exclusion criteria included visible caries in the smile, missing teeth, periodontal disease, or the presence of any severe facial abnormalities. Additionally, patients whose smiles did not allow clear visualization of the lower incisors, the incisal edges, and the widest (mesiodistal) part of the central and lateral maxillary incisors were also excluded.

2.3. Ethical Considerations

The study protocol was reviewed and approved by the Ethics Committee of the Rey Juan Carlos University, Madrid, under internal number (2807202329823). The purpose of the study was explained to the participants, and the confidentiality of the collected information was assured. All patients who agreed to participate in the study signed an informed consent form.

2.4. Procedure

The operator took a frontal photograph of the subjects with a social smile (T0) following the application’s instructions. Subsequently, the subjects followed the steps to modify their smile (T1) (12), resulting in a new image of the subject with a different smile (Figure 1). In their left hand, the subjects held a millimeter ruler positioned at the height of the smile, allowing for digital calibration of the photographs and enabling precise measurement of the smile structures.

2.5. Instruments and Measurements

To ensure reliability and reproducibility, the same operator measured the distances twice using the same photograph, with the second measurement taken several days later to minimize bias. NemoCeph software (NemoStudio20) was used to measure the following variables in millimeters during the T0 phase (initial social smile) and the T1 phase (social smile modified by the SV platform 2024). A millimeter ruler with a precision of 0.01 mm was used within the NemoCeph software (NemoStudio20) for the accurate calibration and measurement of smile characteristics.

Smile width (in millimeters): The width was calculated based on the most lateral visible teeth in the smile. This measurement allowed for the evaluation of the predictability of orthodontic movements involving dentoalveolar expansion or compression. Various studies evaluating dental arch expansion suggest that, to minimize the risk of gingival recession and ensure a high planned movement predictability in the initial setup, applying appropriate biomechanics, the arch width expansion should be limited to a maximum of 2 to 3 mm per quadrant. Therefore, a total expansion of 5 mm was considered to be realistic and predictable movement with aligners.

Vertical exposure of the maxillary central incisor (in millimeters): A randomly selected maxillary central incisor was used to measure the distance from the incisal edge of the tooth to the marginal gingiva or the lowest part of the lip (if gingiva was not visible). The vertical movement of incisors is the most challenging aspect to manage with aligners. In a study addressing open bite cases, an average extrusion of the maxillary incisor of 1.45 ± 0.89 mm was achieved. Other studies have demonstrated that the average intrusion of incisors in deep bite cases ranges from 0.75 to 1.50 mm. Therefore, realistic movement in this study was considered to be up to 1.5 mm of intrusion and 1.5 mm of extrusion.

Width of the maxillary central and lateral incisors (in millimeters): This measurement was taken from the most aligned side. In cases of crowding, the mesiodistal width of a tooth can appear artificially reduced in a 2D image, such as a photograph. To avoid this distortion and ensure more accurate measurements, we selected the tooth on the side where the alignment was closest to ideal. In orthodontics, modifications to the mesiodistal size of a tooth involve a reconstruction or reduction in enamel (interproximal reduction, IPR). The acceptable limit for IPR is determined to be 0.25 mm per side. Enamel reduction exceeding 0.25 mm per side is considered “inappropriate” due to the thickness of the enamel layer and the need to preserve the tooth’s structural integrity. The sample was divided into three groups based on changes in incisor size: cases requiring multidisciplinary treatment (where SV increased tooth width), cases with IPR > 0.25 mm per side, and cases that could be treated solely with orthodontics and aligners (IPR between 0 and 0.25 mm per side).

Proportion of the maxillary lateral incisor width to the central incisor (in percentage): The proportion between the width of the central and lateral incisors was calculated using the previously collected mesiodistal measurements, and the sample was divided into two groups (greater than or less than 0.62) according to the “golden proportion” proposed by Lombardi in 1973 and later developed by Levin.

Anterior gingival exposure level (in millimeters): Measured apically to the crown of the maxillary central incisor; the side with the most exposure was selected. According to the literature, aesthetic gingival exposure is considered to be between 1 and 3 mm (24). The sample was divided into two groups: subjects with more than 2 mm of gingival exposure (tending towards a gummy smile) and subjects with less than 2 mm, as described in the study by Rizzi et al..

Position of the upper and lower dental midlines relative to the facial midline (centered/not centered): For diagnosing dental midlines, the facial midline reference point was a straight line passing through the glabella and subnasal point . The distance from the upper and lower dental midlines was calculated.

2.6. Statistical Analysis

The data collected were analyzed using SPSS version 28.0. Measurement reproducibility was evaluated through Pearson’s correlation coefficient (r) and the intraclass correlation coefficient (ICC). Descriptive statistics were used to assess the frequency of the following variables: smile width, vertical exposure of the labial surface of the maxillary central incisor, and the mesiodistal width of the incisors, as well as the dental midlines. To assess changes in the ratio of the maxillary lateral incisor width to the central incisor and anterior gingival exposure from T0 to T1, a paired t-test was conducted. Significance levels were established at 0.05.

3. Results

3.1. Method Error

The reproducibility of the measurements was evaluated using Pearson’s correlation coefficient (r) and the intraclass correlation coefficient (ICC). A Pearson coefficient greater than 0.9 was obtained for variables such as smile width, vertical exposure of the maxillary central incisor, the proportion between the maxillary lateral incisor width to the central incisor, and the distance between incisal edges. The other variables showed a Pearson coefficient greater than 0.8. The ICC was above 0.8 for all evaluations, indicating high reproducibility.

3.2. Smile Width

Regarding smile width, 58% of the sample showed dental expansion (CI: 58.0 ± 13.77%). Excessive expansion (>5 mm) was observed in 8% of cases (CI: 8.0 ± 7.84%). See Figure 2.

3.3. Vertical Exposure of the Maxillary Central Incisor

In the maxillary arch, 5.9% (n = 3) of the incisors exceeded the range of predictable movement with aligners (>1.5 mm). Excessive extrusion occurred in 3.9% (n = 2) of cases and excessive intrusion occurred in 2% (n = 1) of cases.

3.4. Mesiodistal Width of the Maxillary Central Incisors

For the central incisors, the mesiodistal size was reasonably reduced in 51% (n = 26) of cases, with excessive interproximal reduction in 27.5% (n = 14). Additionally, 21.6% (n = 11) of cases would require multidisciplinary treatment due to an increase in size (Table 1). See Figure 2.

3.4. Mesiodistal Width of the Maxillary Central Incisors

For the lateral incisors, the mesiodistal size was reasonably reduced in 31.4% (n = 16) of cases, with excessive interproximal reduction in 5.9% (n = 3). Additionally, 62.7% (n = 32) of cases would require multidisciplinary treatment due to an increase in mesiodistal size (Table 2). See Figure 2

3.6. Mesiodistal Proportion of Maxillary Central Incisor to Maxillary Lateral Incisor

Initially, 29.4% of the sample had a discrepancy due to either an oversized central incisor or an undersized lateral incisor (<0.62). SV adjusted the proportion to 0.71 ± 0.07, even within the group with a proportion >0.62, with no significant differences (t = 0.044, p = 0.483).

3.7. Anterior Gingival Exposure

Initially, 11.8% of the sample presented a gummy smile. SV tended to reduce gingival exposure in both groups, with a greater reduction observed in the gummy smile group (0.36 ± 0.42 mm vs. 0.09 ± 0.64 mm), although the differences were not statistically significant (t = 1.397, p = 0.09). See Figure 2.

3.8. Upper and Lower Dental Midlines

The upper dental midline was deviated from the facial midline in 25.5% of subjects (n = 13). SV centered the upper midline in 77.9% of these cases. Among the sample, the upper midline was initially centered in 74.5% (n = 38) of subjects, and SV maintained it centered in 84.2% of these subjects. The lower dental midline was deviated from the facial midline in 39.2% of subjects (n = 20). SV centered the lower midline in 65% of these cases. The lower midline was initially centered in 60.8% (n = 31) of subjects, and SV maintained it centered in 67% of these subjects. See Figure 2.

4. Discussion

This research aimed to analyze the outcome of the SmileView™ (SV) simulation in relation to established orthodontic standards. Regarding the transverse dimension, it is well-known that buccal corridors negatively impact smile aesthetics. SV, following the Damon system philosophy of broad smiles, tends to propose expansions in 58.8% of the cases studied. Invisalign®, through SV, seeks to offer an aesthetically pleasing smile, proposing realistic expansions (<5 mm) in 87.5% of cases. However, aligner treatment has limitations to achieving transverse expansion. Studies indicate that only 61% to 70.88% of the planned expansion is achieved, depending on whether the dentition is mixed or permanent. Factors such as the patient’s age and the initial torque of the posterior teeth influence the success of expansion with aligners. In some cases, the use of expanders or palatal disjunctors may be necessary before treatment. Indeed, many studies describe only coronal buccal movement in cases of expansion.

Regarding the exposure of the maxillary incisors, SV is conservative, significantly modifying (>1.5 mm) the vertical position of the central incisor in only 5.9% of cases. This reflects the limitations of aligners in performing intrusion/extrusion movements, which have low predictability (29.6% for anterior extrusion and 35% for anterior intrusion). The effectiveness of orthodontic treatment is influenced by various individual factors that must be carefully assessed before starting the process. It is crucial to measure the initial inclination of the incisor in relation to the bone to avoid having the root move into the cortical bone, which is denser and complicates dental movement during orthodontic treatment. Additionally, other anatomical limitations must be considered, such as the proximity between the tooth root and the maxillary sinus. This proximity can lead to bone remodeling, potentially compromising both tooth stability and periodontal support. It has been observed that proper “staging” during the initial setup can improve predictability. On the other hand, there are alternative methods to influence incisal exposure without moving the anterior teeth, such as changes in molar positioning that affect mandibular rotation. Moreover, relative vertical movements, which are expressions of dental torque, can be combined with other techniques.

When analyzing modifications in the mesiodistal width of the upper incisors, a significant difference was observed in the outcomes of multidisciplinary cases between the central and lateral incisors (21.6% and 62.7%, respectively). This difference could be attributed to the high incidence of microdontia in the lateral incisors. In a study conducted in 2022, it was found that 38.8% of subjects presented with microdontia in at least one of the two maxillary lateral incisors . Traditionally, orthodontists decide whether to maintain or close the diastemas caused by microdontia based on malocclusion criteria such as overjet and Angle’s classification. However, artificial intelligence (AI) appears to favor the reconstruction of the incisor to maintain the ideal dental proportion. This decision would require the intervention of a dental aesthetics specialist and involve additional costs, potentially disregarding the orthodontic criteria mentioned earlier.

In orthodontic practice, significant enamel reduction (greater than 0.5 mm) is employed as a therapeutic technique in cases of macrodontia, where the teeth are excessively large. However, macrodontia is relatively rare in the general population (0.03–1.9%), which does not justify the high percentage observed in the studied sample (27.5% for the central incisor). It is hypothesized that SV may be compensating for the presence of microdontic lateral incisors by adjusting the mesiodistal size of the central incisor to maintain an appropriate dental proportion. This hypothesis highlights the limitations of AI-based diagnostics, which may overlook important epidemiological factors. Another plausible explanation could be the presence of triangular-shaped crowns, observed in 8% of Caucasian subjects. This morphology shifts the contact point towards the incisal edge, creating “black triangles”, a feature associated with dental aging. However, this characteristic is more commonly found in the lower arch. In such cases, the typical orthodontic treatment involves significant reshaping of the crown at the incisal level, followed by the closure of the resulting diastema.

The analysis of the proportion between the central and lateral incisors revealed that SmileView™ (SV) tends to adjust this ratio to 0.72. This result aligns with a study by Kantrong et al., who found a mean ratio of 0.72 in their sample, with an aesthetic preference for a proportion of 0.70. However, there is no universal consensus, as other studies suggest that the “golden ratio” should be 0.66. The discrepancies between the results of different studies could be attributed to the variety of research methodologies employed. Factors such as different measurement techniques for data acquisition and analysis, sample size, ethnic groups studied, and the inclusion of individuals with previous orthodontic treatment may significantly contribute to these differences. Artificial intelligence seems programmed to adjust incisor proportions, but it may not fully account for actual proportions due to inherent biases. For example, in cases of malocclusion, such as incisor rotation, anterior crossbite, or open bite, the measurements would be affected. As a result, the program cannot adapt the final (post-treatment) smile based on the initial situation, but rather tends to adjust all smiles to a predetermined “golden ratio”. This process has limitations and may create unrealistic expectations in patients regarding the cost of treatment. Achieving the simulation’s result often requires the involvement of other dental specialties, which can significantly increase the final treatment’s price.

SV tends to reduce anterior gingival exposure across all analyzed groups, although the difference is minimal and not statistically significant. The reduction is more noticeable in the gummy smile group (0.36 ± 0.42), while in the group without excessive exposure, the reduction is smaller (0.09 ± 0.64). However, this tendency of AI to reduce gingival exposure could have negative effects on patients without gummy smile issues, potentially worsening the smile’s aesthetics, as a slight gingival display is often associated with a more youthful and healthy appearance. Furthermore, the absence of gingival display can result in a flat or straight smile arc, which may be perceived as less dynamic and attractive compared to smiles that show a slight curve or some gingival tissue. The causes of a gummy smile require an orthodontic and/or multidisciplinary diagnosis to determine the appropriate therapeutic approach. Factors such as a short lip, hypermobile lip activity, altered passive eruption, vertical maxillary excess, and gingival hyperplasia are not corrected with conventional orthodontic treatments. These aspects must be considered by the orthodontist before initiating treatment to achieve optimal results. For these reasons, AI does not significantly modify anterior gingival exposure, which is realistic in most cases when using aligners alone.

In the analysis of dental midlines, SV demonstrates greater accuracy in the upper incisors. This is primarily because, during a smile, the upper incisors are more visible than the lower ones, making it easier for the artificial intelligence to locate them in relation to the face. On the other hand, SV’s effectiveness in correcting the lower midline is less reliable. This could be attributed to two main factors: overbite and a significant tendency for lower crowding. Lower crowding, in particular, can make it difficult for the AI system to accurately identify the dental midline, resulting in lower precision in its proposed corrections to the lower arch.

Among the practical implications of this research, it can be stated that SV generates smiles that align with the aesthetic standards established in the literature, making it a valuable tool for orthodontists to promote their treatments. However, it may create confusion among patients by modifying dental proportions without considering the initial anatomical variations. It is crucial to clarify to patients that this is a simulation and not a definitive treatment plan.

With the integration of artificial intelligence in medicine, questions arise about its limitations. In the context of smile simulation in orthodontics, SV might create unrealistic expectations, as orthodontic practice has its constraints. Some dental clinics already include disclaimers on their websites to prevent possible misunderstandings, such as, “It’s important to note that this is purely a simulation, albeit a lifelike one, and the final treatment outcome may differ”.

For orthodontists, SV can be useful in planning dental arch expansions and making decisions to manage cases of microdontia. It would be interesting to study potential patients’ reactions to these simulations to assess their effectiveness as a persuasive tool.

In the future, SV could incorporate 3D imaging, such as cone beam computed tomography (CBCT), soft tissue imaging, and stereolithographic (STL) models of the patient’s mouth, to enhance the accuracy of simulations. It would also be valuable for future versions of the Invisalign SmileView™ platform to incorporate the possibility of human intervention after generating the AI simulation. Allowing the operator to make manual adjustments could better tailor the simulated smile to the patient’s unique dental and facial characteristics. This flexible feature could significantly improve patient satisfaction and treatment predictability.

Given that the use of two-dimensional images in a study analyzing orthodontic factors limits the precision of the results, this research has certain limitations. Dental proportions may be inaccurate if the teeth are not properly aligned, and the U-shaped form of the dental arch implies a depth of field that is not captured in 2D. Although the study’s photographs were taken under optimal conditions, SV is designed to be used with the front camera of a mobile phone, which could result in varying angles and, consequently, different outcomes than those obtained in the study.

5. Conclusions

  1. SV tends to generate simulations of broader smiles that, from an orthodontic perspective, are mostly achievable through aligner treatments.
  2. SV simulations show high predictability regarding the vertical movement of incisors that can be achieved with aligners.
  3. SV adjusts the mesiodistal size of the upper incisors in its simulations.
  4. The software modifies the mesiodistal proportion of the upper incisors, aiming for a “golden ratio” of 0.72, which implies alterations to dental dimensions.
  5. SV’s artificial intelligence does not make significant changes to gingival exposure, although a slight improvement in this aspect is observed.
  6. SV demonstrates the ability to identify and correct deviations in the dental midlines relative to the facial midline. However, there is a greater margin of error in the proposed corrections for the lower arch.

Source

https://pubmed.ncbi.nlm.nih.gov/39585006/

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21 Dec
info

Augmented Reality in Dentistry: Enhancing Precision in Clinical Procedures—A Systematic Review

Abstract

Background: Augmented reality (AR) enhances sensory perception by adding extra information, improving anatomical localization and simplifying treatment views. In dentistry, digital planning on bidimensional screens lacks real-time feedback, leading to potential errors. However, it is not clear if AR can improve the clinical treatment precision. The aim of this research is to evaluate if the use of AR-based instruments could improve dental procedure precision. Methods: This review covered studies from January 2018 to June 2023, focusing on AR in dentistry. The PICO question was “Does AR increase the precision of dental interventions compared to non-AR techniques?”. The systematic review was carried out on electronic databases, including Ovid MEDLINE, PubMed, and the Web of Science, with the following inclusion criteria: studies comparing the variation in the precision of interventions carried out with AR instruments and non-AR techniques. Results: Thirteen studies were included. Conclusions: The results of this systematic review demonstrate that AR enhances the precision of various dental procedures. The authors advise clinicians to use AR-based tools in order to improve the precision of their therapies.

Keywords:  augmented reality; AR; virtual reality; VR; precision medicine; computer-assisted surgical procedure; head-mounted displays; HMD


1.INTRODUCTION

The digitalization that has impacted the dental field in the last decade has brought significant improvements both in clinical workflow and patient comfort. Furthermore, the introduction of augmented reality (AR) in the medical field, in general, has allowed for the clear previsualization of surgical procedures, has simplified the planning of the interventions themselves, and has increased patients comfort.

AR can be defined as the enhancement of sensory perception by adding additional pieces of information that cannot be perceived through the five senses. It is indeed feasible to augment the amount of information obtained through the conventional physical examination, for instance, the precise location of anatomical structures. Concurrently, it is possible to eliminate superfluous data, thereby streamlining the operating field view and, consequently, the therapeutic treatments. This technology should be distinguished from virtual Reality (VR), which immerses the user in a computer-generated environment devoid of any tangible elements.

The digital planning of different interventions is commonly performed in dentistry and maxillofacial surgery on bidimensional screens. The lack of tridimensionality severs the experience since it lacks real-time feedback, resulting, oftentimes, in continuous glances at a screen far from the operative area that may distract the operator and induce errors due to lack of coordination.

Furthermore, a bidimensional image causes the superimposition of spatial information, making the position of overlapping anatomical structures not always distinguishable. This limitation has been overcome by the introduction of head-mounted displays (HMDs), headsets that are positioned right in front of the operators eyes.

AR HMDs may work in two different settings: optical see-through (OST) or video see-through (VST). OST technology allows the user to have a direct view of reality and projected additional data in their line of sight; in VST mode, the virtual content is superimposed over a digital recording of the real world. Real-time cameras are usually mounted on the headset frame, granting a user-like line of sight.

AR/VR technologies with the support of HMD have yielded diverse benefits in the medical field. These include surgery previsualization, which has the potential to enhance clinical outcomes by streamlining the procedure, and for training purposes  Moreover, clinicians can inspect data acquired from 3D examinations of the patient’s face (3D facial scan), oral cavity and teeth (intraoral scan), and skeletal structures (CBCT).

Matching patients’ data allows for the creation of a “virtual patient”, useful not only for clinical analysis but also for therapy planning. These technologies involve oral surgery and prosthodontics at first, with planning and digital manufacturing of prosthetic appliances, both on natural teeth or on implants, based on CBCT data. Planning has improved with extra oral and intraoral scanning, collectively referred to as “patient virtualization”, and it is evolving towards augmented reality. Consequently, the use of these techniques in other fields of dentistry (restorative dentistry, endodontics, oral surgery, and tooth preparation) represents a logical extension of their use.

The adoption of AR-based techniques is occurring rapidly across all medical specialties. In 2020, AR was used in the procedure for inserting pedicle screws in the vertebral column, reducing operative difficulties and proving to be a safe procedure.

Similar results were obtained by Felix et al. in thoracolumbar pedicle screw placement under AR guidance. Zhu et al. utilized an AR-based neuroendoscopic navigation system for intracerebral hematoma localization and remotion, with high accuracy and feasibility [22]. Lecointre et al. described an AR-based robotic assistance system for laparoscopic surgery, performing a real-time multimodal and temporal fusion of laparoscopic images with preoperative medical images in a porcine model system, being a reliable, safe, and accurate system.

AR has found applications in orthopedics and traumatology: Guo et al. proposed a preoperative virtual simulation and intraoperative navigation-assisted fixation AR-based system. It was found that the patient group who underwent the AR system-aided surgery had a significantly shorter operation time and lower blood loss than the conventional surgery group [24]. Chen et al. proposed a navigation system with enhanced arthroscopic information for knee surgery, in which virtual arthroscopic images could reproduce the correct structural information with a mean error of 0.32mm. A systematic review of AR applications in orthopedic surgery found that AR in orthopedic surgery has the potential to be a time-saving, risk-reducing, radiation-reducing, and accuracy-enhancing technique, and that the application of AR technology for intraoperative navigation appears to be well suited to the field of orthopedic surgery.

AR has also provided benefits in craniomaxillofacial surgery and skull base surgery by overcoming the challenges of traditional navigation systems, such as hand–eye coordination and depth perception, improving ergonomics and visualization. In hepatobiliary and pancreatic surgery, intraoperative information generated from an AR system provided useful navigation assistance. In addition to surgical fields, AR has been applied to the treatment of psychologic disorders, cognitive impairment, and motor rehabilitation. A recent systematic review stated that AR technology has been shown to improve ergonomics and visualization, as well as reducing operation time and blood loss in minimally invasive surgery procedures, even though the examined studies have been limited to experimental developmental approaches, while the number of clinical trials and systematic reviews is low.

AR offers significant advantages in dental education by providing an immersive and interactive learning experience. By overlaying digital information onto real-world images, students can visualize complex anatomical structures in detail and simulate surgical procedures in a safe and controlled environment. This approach enhances the understanding of complex concepts, improves visual and kinesthetic memory, and allows students to acquire practical skills more effectively. Additionally, the ability to repeat simulations without risks to patients enables students to progressively refine their clinical skills, better preparing them for real-world practice.

However, it is not clear if AR can improve clinical treatment precision. The aim of this systematic research is to ascertain whether the utilization of AR-based instruments can enhance the precision of dental procedures. Precision is a critical factor in improving treatment outcomes, reducing errors, and minimizing invasive interventions. By focusing on precision, the review aims to assess how AR contributes to the refinement of dental procedures, ultimately improving patient care and optimizing clinical workflows. This review is necessary to compile and analyze existing evidence, assess the precision improvements AR can bring to various dental procedures, and identify gaps where further research is needed.

2. Materials and Methods

2.1. Search Strategy

The systematic review was carried out on electronic databases, including Ovid MEDLINE, PubMed, and the Web of Science. No searches of other databases, conference proceedings, or gray literature were performed, as the focus was placed on peer-reviewed studies from well-established databases to ensure a high level of evidence quality and consistency. The date parameter of the paper collation was set from January 2018 to June 2023. The following terms and their combinations were searched: (augmented reality) AND ((dentistry) OR (oral surgery) OR (endodontic) OR (prosthodontic) OR (dental restorative) OR (periodontology) OR (orthodontics) OR (orthognatic), to which “Boolean operators” were applied. The keywords were selected to gather and register as much relevant data as possible.

The search string used was (“augmented reality”) AND (“dentistry” OR “oral surgery” OR “endodontic” OR “prosthodontic” OR “dental restorative” OR “periodontology” OR“orthodontics” OR “orthognathic”) AND (date: [1 January 2018 TO 30 June 2023]).

The following focus question was developed, according to the population, intervention, comparison, and outcome (PICO) study design: “Does the use of AR-based instruments (I) increase precision (O) of dental interventions (P) compared to non-AR techniques (C)?”

2.2. Eligibility Criteria

The full texts of all possibly relevant research papers were chosen, considering the following inclusion criteria:

  • Studies comparing variation in the precision of interventions carried out with AR instruments and non-AR techniques.

All types of study designs, including clinical trials, observational studies, case reports, and case series, were considered to ensure the inclusion of a broad range of evidence. This comprehensive approach was adopted to capture as much relevant data as possible on the use of AR in dentistry, allowing for a more thorough and representative analysis of its impact on precision across various dental procedures. The exclusion criteria that were considered were as follows:

  • Research that evaluates the effects of AR instruments without comparing with non- AR techniques;
  • Reviews and meta-analyses.
  • Papers without the full text being available.
  • Papers not in English language.

English studies may not be peer-reviewed at the same level, further complicating their inclusion. This decision ensures consistency in the quality of the reviewed literature and maintains the accuracy of the analysis.

2.3. Study Selection and Data Extraction

To reduce bias, two researchers from Messina University (F.P. and G.L.G.) independently conducted the literature search. In cases where there were discrepancies in the results, these were first addressed through thorough discussion between the two reviewers. If a consensus could not be reached, a third senior researcher (R.L.G.) was consulted to resolve the issue. This procedure was applied at each critical phase of the review process, including initial screening, assessment of eligibility for final inclusion, data extraction and analysis, and quality assessment. By employing this structured, three-step approach, we ensured that the study selection process remained rigorous, transparent, and free from bias, enhancing the overall reliability of the review. The following variables were defined in this investigation: author and year, intervention, object of experimentation, technique, field of interest, conclusions.

2.4. Risk of Bias Assessment

The evaluation of in vitro studies was set up with a methodological index that uses a checklist for in vitro studies on dental materials (CONSORT). This checklist of items has the purpose of evaluating how the study was designed, analyzed, and interpreted, and uses 14 domains [41]. In vivo studies were assessed according to ROBINS-I [42]. ROBINS-I evaluates risk of bias across 7 domains: confounding, selection of participants into the study, classification of interventions, deviations of intended interventions, missing data, measurement of outcomes, and the selection of reported results. For in vivo studies, risk of bias elements were rated as “yes”, “possibly yes”, “no”, “possibly no”, “no information”, or “not applicable”. Non-RCTs were then classified using the ROBINS-I classification as “low”, “moderate”, “serious”, or “critical risk of bias” depending on whether the extent of bias in the domains could have resulted in a significant bias in the outcomes of interest. The CONSORT checklist for in vitro studies and the ROBINS-I tool for in vivo studies were applied point by point to evaluate the risk of bias in each study. Two independent reviewers (F.P. and G.L.G.) systematically completed these checklists by answering each item in accordance with the criteria outlined by the tools. In cases where there were discrepancies in the evaluations, a third senior reviewer (R.L.G.) was consulted to resolve them. This process ensured that each study was rigorously assessed for bias in a consistent and structured manner. The results of these assessments are summarized in the accompanying tables and further discussed in the subsequent sections.

3. Results

3.1. Study Selection

The initial search on scientific search engines yielded 615 results. Duplicate research and studies published before 1 January 2021 were excluded, resulting in a total of 262 studies. Out of these, 65 articles were excluded as they were reviews, meta-analyses, case reports, communications, and congress papers. After the initial selection, 197 studies underwent a full-text examination. Among these, 42 articles were discarded because they used AR for educational or training purposes, 54 used AR in fields other than dentistry, 19 used AR but did not compare it with non-AR techniques, and 69 were not aligned with the article. In total, 13 studies were included in this review (Figure 1). The included papers are listed in Table 1.

3.2. Risk of Bias

Tables 2 and 3 present the risk of bias in the in vitro studies and randomized clinical trials (RCTs). While the results of the risk of bias assessment are presented in the accompanying tables, the primary sources of bias identified across the studies included selection bias, performance bias, and detection bias. Selection bias occurred in studies where the participants were not randomly selected, potentially skewing the results. Performance bias was observed in studies where it was not feasible to blind the participants or clinicians, which may have led to overestimations of the effectiveness of the augmented reality (AR) interventions. Additionally, detection bias was noted in studies that lacked standardized methods for measuring outcomes. These sources of bias may affect the reliability of the results, particularly in evaluating the precision enhancements brought by AR in dentistry. Therefore, caution is needed when interpreting these findings, and future studies should aim to minimize these biases to provide more robust evidence.

In this review, effect measures such as risk ratios or mean differences were not applicable due to the nature and heterogeneity of the included studies, which did not allow for a quantitative synthesis like a meta-analysis. The primary aim was to assess precision improvements in dental procedures using augmented reality, and the diversity of study designs, outcomes, and interventions made direct comparisons challenging. However, where possible, individual study findings related to precision were summarized, and effect sizes were reported qualitatively rather than quantitatively. The narrative synthesis approach was employed due to the diversity of the included study designs, methodologies, and outcome measures. The results of individual studies were combined by identifying common themes, such as the impact of augmented reality on procedural accuracy, patient outcomes, and clinical workflows. Studies were grouped based on their interventions and outcomes, and the key findings were synthesized to highlight patterns and trends in the use of augmented reality across various dental procedures. This approach allowed us to provide a comprehensive overview of the current evidence, despite the heterogeneity of the studies.

Due to the substantial heterogeneity in study designs, outcome measures, and interventions, it was not feasible to conduct a meta-analysis. While individual studies contributed valuable insights into the application of augmented reality (AR) in dentistry, the lack of standardized precision metrics and consistent effect measures made direct comparisons challenging. The included studies varied significantly in their methodology, particularly in how precision was defined and measured, which further complicated any attempt at quantitative synthesis. Therefore, the review adopted a narrative synthesis approach, summarizing and grouping the findings by key themes such as procedural accuracy, patient outcomes, and clinical workflows.

4. Discussion

Over the last decade, interest towards medical augmented reality (AR) has increased greatly, fueled by the prospect of developing instruments capable of improving clinical precision.

This technology could provide significant benefits in accuracy and control during clinical procedures. However, in the dental field, AR uses are still limited. Nowadays, dental software is developed for implant placement assistance, while other

fields of dentistry have not benefited from this technology at the same pace as implant dentistry. At present, there are no commercial AR instruments for assisting in other fields of dentistry (endodontics, restorative dentistry, orthodontics, prosthodontics, periodontal surgery, etc.) but only experimental software. This highlights the need for more specific research to explore and make the most of AR technology’s potential in dentistry. The research included in this systematic review mainly consisted of in vitro studies. One in vivo study was also found. Four studies were considered to

have a high risk of bias due to the lack of sample randomization, the absence of blinding in the study, and the omission of a power analysis for determining the sample size.

These factors significantly compromise the internal validity of the studies, as they increase the likelihood of systematic errors and reduce the potential of obtaining reliable and generalizable

results. Nine studies were assessed as having a low risk of bias. The bias evaluation of the prospective clinical study included in this review gave a result of a “serious risk of bias”. This judgment is due to significant issues identified across various domains, including inadequate control of confounding, potential deviations from the intended intervention, and the risk of selective reporting of effect estimates based on multiple analyses. These factors indicate that the study has important problems that may affect the reliability of its results.

4.1. Implant Dentistry

Recently, applications of AR in the dental field have become available for clinical application. These instruments, such as Navident (ClaroNav, Toronto, ON, Canada), Xguide (X-nav technologies, LLC, Lansdale, PA, USA), ImplaNav (ImplaNav, BresMedical, Sydney, Australia), and DENACAM (mininavident AG, Liestal, Switzerland) are used for guided implant surgery. As a natural evolution of static guided implant surgery and thanks to advancements in AR technology, the concept of “dynamic guided implantology” (or “navigated implantology”) has emerged. The planning process follows a similar workflow to that of guided surgery, with an implant project planned based on CBCT imaging, from which a virtual guide is derived. The technique involves first planning the implant procedure using three-dimensional images generated from CBCT. Subsequently, the procedure is carried out using a specific surgical handpiece whose position relative to the patient’s oral cavity is continuously tracked by a system of video cameras, displaying the exact position and angulation of the surgical drill on a screen for the clinician to see.

Four articles included in this research use AR systems in implant dentistry; one of these uses Navident (ClaroNav, Toronto, ON, Canada) plus a new AR system designed by researchers. The remaining studies (three articles) use systems designed for research purposes.

Kivovics M. et al. and Liu et al. confirm that their AR system designed for implant dentistry allows for better spatial precision (closer real implant position to CBCT implant placement planning) than a freehand technique. Other researchers obtained conflicting results . González-Rueda JR et al. demonstrated that the conventional freehand technique provides greater accuracy in the placement of zygomatic dental implants than the static computer-assisted implant surgery technique, dynamic computer-assisted implant surgery technique, or augmented reality techniques. In this research, the authors placed the implants in standardized resin blocks, using AR-based techniques, static navigation and dynamic navigation (Navident), and a freehand technique. The results, as explained by the authors, may be justified because the zygomatic dental implants assigned to the freehand control group were the last to be placed, which meant the operator was able to learn and memorize the correct position of the zygomatic dental implants [50]. Santiago Ochandiano et al. evaluated the implant positioning precision in oncologic patients who underwent free flap reconstruction. These patients had their anatomy compromised by previous surgeries for head and neck tumors. The authors used three different techniques for implant placement: static navigation, dynamic navigation, and a combination of static and dynamic navigation; the static–dynamic navigation group obtained the best precision. The observed inaccuracy in the dynamic navigation group is mainly due to the use of a toothsupported silicone jig. The jig’s instability represented an inaccuracy risk. The authors report that, although the jig allowed for the correct registration, its intraoperative instability led to problems in maintaining the planned position. The jig instability led to involuntary movements during the surgery, making the maintenance of dynamic navigation difficult and causing positioning errors. It must be observed that the patient population consisted of individuals with large anatomical distortions.

Placing the implant in the right position significantly impacts the long-term stability and success of implant-supported restoration, particularly in cases where bone availability is limited.

4.2. Endodontics

The advancements in endodontics over recent years can be attributed to the enhancements in NiTi alloys utilized in instruments, which have facilitated minimally invasive access cavities and shaping that more accurately reflects the canal anatomy . No advances have been made that improve the precision in locating the endodontic chamber or the endodontic canals. Two articles regarding AR-aided endodontic access cavities were included in this systematic review.

Faus-Matoses et al. positioned extracted single canal teeth in a resin block. They performed the planning of the endodontic access cavity based on a CBCT evaluation, took a scan of the resin block with teeth, and matched the DICOM and STL files. The access cavities were t en prepared using the previously planned insertion axis, which is displayed in real time on an HMD (Hololens2, Redmond, WA, USA).

Fangjie Li et al. created an AR system in which root canal therapy is performed visualizing CBCT slices on an HMD, with the aid of a marked high-speed handpiece and mirror, after preoperative planning [54]. Both articles confirm that access cavities performed using dynamic navigation systems are more accurate than those performed using a freehand technique, whether the access cavity is carried out by an expert endodontist or by a non-expert endodontist [47,54]. The use of data obtained from CBCT could be useful to overcome obstacles that may form during the irrigation phases. One study evaluates AR systems for endodontic surgery. Bosshard F.A. et al. created an AR system for endodontic surgery (apicectomy), demonstrating the reliability of the technique compared with static guided surgery.

4.3. Orthodontics

Three articles included in this systematic review used AR systems to evaluate the precision of orthodontic miniscrew insertion. After the information obtained from the CBCT images and navigation system was combined on the display device, the AR-aided system indicated the planned miniscrew position to guide the clinicians during the placement of miniscrews, improving the accuracy of miniscrew placement. In all the included research, the AR-aided systems improved the accuracy of miniscrew placement regardless of the clinician’s level of experience.

4.4. Tooth Preparation

Two studies included in this systematic review evaluate the use of AR for tooth preparation. The protocol developed by Obispo C. et al. foresees the creation of an acrylic resin 3D printed dental arch with 10 dental elements. Subsequently, ideal tooth preparations for complete crowns were digitally planned using dental planning software according to the tooth preparation guidelines established. Five elements were prepared with a freehand technique, the other five using an AR system that allows for HMD visualization of the virtually planned tooth preparation designs (Hololens1, Redmond, WA, USA) and their superimposition on the resin model. Kihara et al. evaluated the possibility of the implementation of an AR system in order to guide clinicians in tooth preparation, substituting silicone indexes. The system superimposed an ideal abutment shape on a model. The results reported in these articles showed that the computer-aided preparation technique using an augmented reality appliance provided a more accurate preparation design than the freehand preparation technique for complete crown preparation, a more conservative approach with less over-reduction.

4.5. Oral Surgery

A study included in this research evaluated the reliability of an AR system compared to a freehand technique for monoradicular tooth autotransplantation. In this experiment, extracted teeth were mounted on epoxy resin models. The researchers then created “pockets” in the model, allowing transplantation. After digital planning, the teeth were transplanted and subsequently evaluated with a CBCT examination in order to estimate the coronal, apical, and angular deviations. The use of AR allowed for better precision compared to the conventional technique for dental autotransplantations, particularly for apical deviation. These findings suggest that AR could be a promising technique for improving dental autotransplant success.

4.6. Result Evaluation

The analysis of the studies included in this review demonstrated that the use of ARbased instruments determines a significant precision improvement in dental therapies compared with non-AR-based techniques. The use of AR allows for better accuracy in implant surgery, in endodontic access cavity preparation, and in orthodontic miniscrew placement and tooth preparation, with a more accurate and conservative approach. These findings confirm AR’s potential to revolutionize the therapeutic approach in dentistry, with instruments that enhance accuracy and can reduce intraoperative complications or therapeutic failures.

However, a significant limitation that emerged in this review is the diversity of AR systems used in research. Every study analyzed different software and hardware; this can make it difficult to draw generalizable conclusions. In fact, some instruments may be more or less accurate if compared to similar ones. Plus, the technology that constitutes an instrument may be completely different from another. This lack of standardization underlines the need for unified and efficient AR instrument development, appositely designed for clinical use. Finally, for the majority of AR-based instruments, there needs to be a match between STL data obtained from an intraoral scan and data from a CBCT exam; this determines an increased radiation dose to the patient, even when CBCT scan is not justified, constituting an important limitation of the technique. Recently, an ARbased tool for tracking teeth in a video image for bracket positioning was developed, encompassing this limitation. This innovative approach eliminates the need for CBCT imaging or physical guides, making it a safe and convenient option for clinical use by overlapping the digitally planned bracket position over the patient’s clinical crown for a precise recommendation for bracket positioning. The authors demonstrated the feasibility of the technique but did not measure its accuracy; consequentially, it is not possible to estimate the suitability of this AR-based system.

Augmented reality (AR) has not yet been widely applied across all fields of dentistry. For instance, in pediatric dentistry (pedodontics), AR’s potential remains largely untapped.

 However, recent research has begun exploring its use in this field. A notable example is a study that presented a protocol for developing a serious game aimed at motivating children to practice good oral hygiene habits using AR technology. This innovative approach leverages AR to engage children in an interactive and educational experience, helping them understand the importance of oral hygiene in a fun and immersive way. While the use of AR in fields like pedodontics is still in its early stages, such initiatives highlight its potential for improving patient education and compliance, particularly among younger populations. This also suggests that AR could play a significant role in preventive dentistry, especially in promoting good oral hygiene practices from a young age.

Augmented reality (AR) has also been utilized as a non-pharmacological tool to manage chronic or post-operative pain. According to the findings of a systematic review, AR, alongside virtual reality (VR) and mixed reality (MR), has shown promising results in improving pain-related outcomes in various clinical settings. These technologies function by diverting patients  attention from pain stimuli through immersive experiences, thus promoting pain relief through distraction and cortical re-patterning mechanisms. While most studies observed short-term pain reduction immediately following the intervention, more research is needed to confirm the long-term benefits and address potential accessibility challenges. Moreover, AR-based pain management interventions could also contribute to improving mental health by reducing stress and anxiety associated with chronic pain.

Future research might focus on standardized system experiments to allow for a direct comparison between studies, to further validate the AR accuracy in in vitro studies, eliminating the need for CBCT data, limiting ionizing radiation exposure to clinically relevant cases.

4.7. Limitations

Most of the studies selected for this research are in vitro studies on models, which limits the clinical applicability of the findings. Therefore, the conclusions of this research may not fully translate into clinical practice. Additionally, the high prevalence of selection, performance, and detection biases across studies may have impacted the reliability of the results. The absence of standardized outcome measures and precision metrics across studies further restricted our ability to conduct a meta-analysis. As a result, our conclusions are primarily based on a qualitative synthesis, which, while informative, does not provide the level of evidence that a quantitative synthesis, such as a meta-analysis, could offer.

Another limitation is time, as AR technology in medicine is a continuously evolving field with rapid advancements in new technologies and devices. The devices or software used in the selected studies may now be outdated or represent outdated versions, potentially explaining some of the discrepancies in the results.

Future research should focus on mitigating these biases by employing more rigorous study designs and establishing standardized outcome measures for precision. This would enable more robust comparisons and allow for a quantitative synthesis to evaluate the overall effectiveness of AR in dental procedures through meta-analysis. Moreover, the field requires well-designed clinical trials to validate the findings observed in vitro and to confirm AR’s benefits in real-world dental practice.

While AR technology presents several potential benefits for enhancing precision and improving outcomes in dental procedures, it also poses certain limitations and risks that must be considered. One of the primary limitations is the high cost associated with the acquisition and implementation of AR systems in dental practices. The technology itself, including the hardware and software, may require significant financial investment, which can be a barrier for smaller clinics or practitioners in developing regions.

From a technical standpoint, AR systems rely on the accuracy of real-time data and the seamless integration of virtual elements with physical environments. Inaccuracies or delays in the AR interface could lead to procedural errors, potentially compromising patient safety.

Furthermore, the learning curve for clinicians to effectively use AR systems can be steep, particularly for those who are less familiar with digital tools. This could result in increased procedure times and reliance on the technology, potentially reducing the manual skills of practitioners over time.

In addition to technical and financial concerns, AR also introduces psychological and ergonomic risks for practitioners. The continuous use of AR systems, especially involving wearable devices like headsets or glasses, could contribute to mental fatigue and visual strain, impacting the practitioner’s overall performance. There is also a risk of cognitive overload, where the amount of information presented in real-time through AR systems may overwhelm clinicians, making it difficult to focus on the procedure at hand. In extreme cases, this could contribute to stress and anxiety, potentially affecting mental health.

While adverse events related to AR in dentistry are not well documented, early evidence from other medical fields suggests that the extended use of AR tools could lead to a range of ergonomic and mental health issues, including discomfort, disorientation, and burnout. These risks highlight the importance of balancing the use of AR technology with traditional methods, ensuring that clinicians are not overly dependent on these systems and that they receive adequate training and support.

In conclusion, while AR offers promising advancements for dentistry, it is crucial to consider these limitations and risks when implementing the technology in clinical practice. Future research should focus on understanding the long-term effects of AR use on both procedural outcomes and the well-being of healthcare providers.

5. Conclusions

The conclusions drawn from this review are consistent with the evidence presented, highlighting the potential of augmented reality (AR) to enhance precision in dental procedures. AR can be applied in clinical practice to improve accuracy in surgical interventions, such as implantology and orthodontics, by providing real-time visual guidance. Additionally, it offers the potential for better training tools in dentistry, allowing practitioners to simulate complex procedures with greater precision. However, several limitations must be acknowledged before AR can be widely integrated into clinical practice. Current AR systems are still in the developmental stages, and the technology’s cost, along with the lack of standardized systems, poses challenges for widespread adoption. Furthermore, many of the studies included in this review are in vitro, limiting the direct application of the findings to real-world clinical settings. To fully realize the potential of AR in dentistry, future research should focus on validating these technologies in clinical trials, developing user-friendly interfaces, and addressing cost and accessibility issues. By overcoming these limitations, AR could become a powerful tool in modern dentistry.

Source

https://pubmed.ncbi.nlm.nih.gov/39585006/

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16 Oct
info

Formative feedback generation in a VR-based dental surgical skill training simulator

Abstract

Fine motor skill is indispensable for a dentist. As in many other medical fields of study, the traditional surgical master-apprentice model is widely adopted in dental education. Recently, virtual reality (VR) simulators have been employed as supplementary components to the traditional skill-training curriculum, and numerous dental VR systems have been developed academically and commercially. However, the full promise of such systems has yet to be realized due to the lack of sufficient support for formative feedback. Without such a mechanism, evaluation still demands dedicated time of experts in scarce supply. To fill the gap of formative assessment using VR simulators in skill training in dentistry, we present a framework to objectively assess the surgical skill and generate formative feedback automatically. VR simulators enable collecting detailed data on relevant metrics throughout a procedure. Our approach to formative feedback is to correlate procedure metrics with the procedure outcome to identify the portions of a procedure that need to be improved. Specifically, for the errors in the outcome, the responsible portions of the procedure are identified by using the location of the error. Tutoring formative feedback is provided using the video modality. The effectiveness of the feedback system is evaluated with dental students using randomized controlled trials. The findings show the feedback mechanisms to be effective and to have the potential to be used as valuable supplemental training resources.

Keywords: Virtual reality, Dental skill training simulator, Formative feedback, Objective feedback, Video-based feedback.


INTRODUCTION

Surgical skill training has been traditionally based on the Halstedian apprenticeship model whereby the surgical trainee performs a task with guidance and close supervision from an expert surgeon [18], [58]. But several factors, including patient safety concerns, shortened training programs, limitations on available operating room time, and desire for standardization have strained this model [36]. In Dentistry, increasing enrollments and a shortage of experienced instructors, have resulted in high student-to-tutor ratios [61], which in turn means that students often do not receive as much supervised training as would be desirable and can end up with unsupervised practice during training. Even when an assessment is carried out, it is by nature subjective, lacking sufficient standardization [39], [51]. As a result, the past two decades have seen an increase in the use of simulation-based training [33] to provide trainees with increased training time and the skills needed to perform complex operations before practicing them on live patients. Recent years have seen the proliferation of VR-based dental simulators due to enabling technological advancements, combined with concrete benefits of the approach [16], [24], [34], [55]; Dangxiao [57]. VR simulators offer high-fidelity simulations that are reusable and can be configured to provide trainees practice on a variety of different cases [5]. They also have the ability to record accurate data on individual performance, which provides the opportunity for trainees to practice independently and receive objective feedback [42]. While many existing VR surgical simulators provide feedback, the feedback typically concerns the outcome and/or procedural kinematic parameters, with no linkage between them. While such feedback can explain the differences between student and expert performance and/or the distance to the ideal performance, it lacks the essential causal information about actions and their desirable or undesirable effects. This type of feedback is known to be essential in effective training of psychomotor skills [26], [47].

In this paper, we present a formative feedback system which objectively assesses surgical skill and generates feedback in a VR dental simulator. The feedback prototype was developed for the access opening procedure of endodontic root canal treatment. Endodontics is one of the most challenging areas of dental surgery and it can be associated with unwanted or unforeseen procedural errors [50]. A variety of procedural errors could contribute to reported failure rates as high as 32.8% [64]. Alhekeir and colleagues [2] with a self-report design showed 68% error rate by senior students. In terms of quality, less than 50% of root filling is found to have acceptable quality [28], [43]. A suboptimal standard of root canal in undergraduate training could translate into poor quality of treatment outcome and inferior standards care in providing the treatment to the actual patients [12]. Inadequately treated teeth commonly feature iatrogenic errors such as ledges, perforations, and apical transportation [28], [43]. Perforations in endodontics can occur during access cavity preparation and mechanical instrumentation of the root canals [14]. The healing rate in teeth with perforations was 30% lower than in teeth without perforation [7]. Whilst the majority of evidence has focused on repairing perforations using various materials [35], the evidence on prevention and training is limited [50]. Although the gap has long been noticed, the issues are not fully addressed anywhere. The first step towards increasing the level of patient safety in endodontic treatment is for all clinicians to acquire knowledge and skills in the early stage of training. Such skills are best learned with deliberate practice with sufficient objective formative feedback. Using a VR dental skill training simulator, we generated formative feedback by correlating the information concerning errors and the portions of the procedure responsible for them and then communicating the information using an augmented playback modality. The tutoring feedback enables students to learn to associate their actions with the resulting performance.

2. Methodology

We focused on the automatic generation of feedback for surgical skill training in a dental VR simulator. While we are interested in developing general techniques, a specific domain is required to demonstrate and evaluate the approach in a rigorous way. While many details of the system are specific to the chosen dental surgical procedure, fundamental elements of the framework generalize to other surgical domains.

2.1. Simulator and domain

We employed the VR dental simulator developed by Rhienmora et al. [41]. The simulator operates on a standard PC connected to two GeoMagic Touch haptic devices [1] which control the dental handpiece and dental mirror (Fig. 1). A monitor is placed at eye level, and the haptic device is positioned at the elbow level directly in front of the participant. A virtual high-speed handpiece with a tapered bur of diameter 1 mm and length 6 mm is employed. The tooth model is acquired using three-dimensional micro-CT (RmCT, Rigaku Co., Tokyo, Japan). In the simulator, the mandibular right molar tooth is stored in the form of a three-dimensional grid of voxels representing the density of the structure at each point using a value between 0 and 255, with 0 representing an empty voxel. When the bur collides with the tooth, the force transmitted through the haptic device is a function of the density values of the colliding tooth voxels, with higher density values producing larger counterforces. The operator receives different force feedback depending on the density value of the tissue while cutting the tooth. A study of the construct validity of the simulator showed the haptic force feedback to the operator to be similar to working in the real situation [49] and a second study demonstrated the transferability of learned skills [50].

We selected the access cavity preparation phase of the root canal treatment procedure on the mandibular right molar (Vertucci’s type VIII root canal configuration [54] to demonstrate and evaluate our approach. This procedure was chosen because it exclusively involves drilling, which is supported by the simulator, and because fine motor skill is essential to achieve an optimal outcome. In this phase, the endodontist drills a small access hole through the surface of the tooth crown to gain access to the pulp chamber and root canals for treatment. The ideal shape of the opening is a function of the tooth shape, tooth size, and the number and location of the root canals. The number and location of the root canals can differ in the same tooth across different patients. The ideal result of access opening preparation is to create an unobstructed passageway to the pulp space and the apical portion of the root canals (Fig. 2, red-colored outline) without needlessly removing excess tissue.

During data collection, data was gathered on elapsed time and kinematic variables concerning

  • the position of the handpiece in x, y, and z-axes,1
  • the angulation of the handpiece with respect to x, y, and z-axes,
  • drilling enabled/not enabled,
  • the position of the mirror in x, y, and z-axes,
  • the angulation of the mirror with respect to x, y, and z-axes,
  • the force applied on the handpiece in x, y, and z-axes.

Variables were collected from the beginning of the procedure until the end in the kinematic procedure log.

Access preparation consists of three distinct stages (Fig. 3):

  • Stage 1, initial drilling to shape the outline;
  • Stage 2, extending the opening to the distal canal orifice;
  • Stage 3, extending the opening to all the remaining canal orifices.

We approached feedback generation as a credit assignment problem [31], which is the problem of assigning credit or blame for outcomes of a procedure to specific actions in that procedure. Our approach makes use of the spatial information of errors to obtain the associated temporal information of actions. The approach is robust and applicable to the tasks involving both hard and soft tissue simulation. We evaluate the effectiveness of the feedback system using randomized controlled trials with dental students. We measure the learning gains between three groups of participants: a group trained without using a training simulator, a group trained with the simulator without the feedback system, and a group trained with the simulator with the feedback system.

3. Formative feedback system

As shown in Fig. 4, our approach to formative feedback begins with an assessment of the procedure outcome to identify the location, the type, and the severity of errors. To determine the portions of the procedure responsible for errors, the way the procedure carried out by the student is assessed. In the subsequent step, the relation between procedure and outcome is used to provide feedback in the following step.

The major components of the feedback system are (i) Automated outcome scoring system to assess the outcome, (ii) Correlator to carry out the assessment of the procedure and perform the correlation between procedure and outcome, and (iii) Feedback generator to provide formative feedback.

3.1. Automated outcome scoring system

The automated outcome scoring system evaluates and assigns scores to the outcome to identify the types and locations of errors in the outcome. We use a general scoring algorithm [63] for outcome evaluation in dental procedures. The score cube-based outcome scoring approach starts by creating three virtual templates to define the maximum, optimal, and minimum acceptable drilling regions. The virtual templates are applied to the score cube volume where each voxel is assigned a score given by its proximity with the templates. The voxel scores are weighted based on their relative importance in defining the severity of the errors. The student’s drilling area is then extracted, and the weighted scores are obtained by mapping the drilled area onto the score cube. The scores are computed for four axial walls: Distal, Mesial, Lingual and Buccal walls, and the pulp floor (Fig. 5).

The overall outcome score is computed as the average of axial walls and the pulp floor scores. Also, an error log detailing the types of errors, and the locations of the errors in the outcome are available from the outcome scoring system. Since providing feedback at the lowest voxel level is not useful, the voxel level error information from the automated outcome scoring system is grouped into regions of under- and over-cutting. Based on discussions with an expert and through experiments, the clusters consisting of less than 50 voxels are considered as minor errors which do not contribute to the performance and are discarded from further analysis.

3.2. Correlator

The formative feedback in our study addresses three aspects of errors: the type (what), the location (where), and the time they were committed (when). In practice, several mishaps or errors can arise during the access opening stage of root canal treatment, including unidentified root canals, damage to existing restoration, under-/over-extension, perforations, and crown fractures [21]. We limit our focus to the three most common types of errors:

 

  • Undercut: when the dentist drills a hole with a small diameter, the roots remain inaccessible
  • Overcut: when the dentist removes more tooth mass than necessary
  • Perforation: when the dentist accidentally drills a hole through the surface of the tooth.

Once the errors in the outcome are localized, the next step is to identify the actions in the procedure responsible for each error. However, there could be more than one underlying cause (action) that contributed to each error in the outcome. To provide formative feedback, it is necessary to understand the underlying actions that could lead to errors. For each operative error, we determine the possible causes as follows.

  • Overcut error

As shown in Fig. 6, the overcut case occurs when the student’s drilling reaches the area beyond the maximum area defined by the templates Fig. 6 (a). In the resulting outcome, the overcut regions will be recognized as an overcut error (the filled area in Fig. 6 (b). Although the filled area is labeled as an error, the actions taken in that region could not be immediately labeled as wrong actions because the following conditions could lead to overcut errors (Fig. 6(a–d)).

  • An improper amount of force was exerted on the instrument.
  • The instrument had an incorrect orientation.
  • The right amount of force was applied, but the student did not recognize the area to stop drilling and repeatedly drilled in the same region.
  • Repeated drilling in the same neighborhood eventually leads to an overcut error in that area.
  • Undercut error

In contrast to overcut, undercut cases occur when the student did not clear the internal tooth anatomy entirely as required. The undercut regions can be determined by the optimal template as shown in Fig. 7 (a). As shown in Fig. 7 (a-c), the undercut errors can be caused by

 

  • An improper amount of force was exerted on the instrument.
  • The instrument has an incorrect orientation.
  • An insufficient number of passes at the student’s drilled area prevented him from reaching the optimal drill area.
  • Perforation error

The perforation case occurs when the student’s drilling reaches beyond the maximum template, and the instrument punches through one of the tooth walls, resulting in an irreversible hole in the wall as shown in Fig. 8 (a–d). The causes of perforation errors are the same as for overcut errors.

Overcut errors occur from over drilling of the tooth (excessive drill actions), while undercut errors occur at the area of the tooth where the trainee did not drill as required (omitted drill actions). Consequently, drill actions associated with undercut errors cannot be identified in a straightforward manner. Therefore, we identify the nearest drilled regions of the undercut errors and perform the analysis on the drilling actions of the procedure in those regions to assign the blame for the undercut error.

For some errors, the responsible portions of the procedure can be identified based on the information obtained by correlating the outcome and the procedure. On the other hand, some errors caused by a consequence of more holistic characteristics of the procedure such as the incorrect tool angulation throughout one stage due to incorrect finger positions and misunderstanding of the sequence of stages, are more challenging to identify and are not addressed in this current formative feedback system design.

3.3. Procedure and outcome correlation

The correlator needs to identify when and how each error was made during procedure execution for each error diagnosed in the outcome. To determine when the error occurred, the spatial locations of error voxels in the outcome are mapped to the collided voxel log to identify the portion(s) of the procedure responsible for each error. Similar to the kinematic procedure log, the collided voxels are recorded every 1000 ms while the procedure is performed in the simulator. The log contains the timestamps and the locations of voxels with which the instrument collided during the procedure.

Actions over multiple parts of a procedure may be responsible for a single error. Some error regions may also spread across more than one wall, and a single wall may contain more than one error. To map the errors with the portions of the procedure, the correlator must obtain the timestamps at which the error voxels are drilled. Using the log of collided voxels over time, the temporal information of each voxel is gathered, and the portions of the procedure associated with the errors are identified as shown in Fig. 9. Fig. 10 shows an example of mapping between the error voxels from error information from outcome scoring and the collided voxel log.

For the overcut and the perforation error cluster types, the error-related timestamps are directly obtainable using the error voxels since the errors are caused by the drilling actions. For the undercut error clusters, the mapping cannot be done straightforwardly due to the absence of collided voxels. Therefore, for each undercut cluster, the correlator first looks up the nearest drilled area. From the voxels of the nearest drilled area, the timestamps from the collided voxel log are gathered for mapping. The large size of the log files can cause the mapping process to take significant time. To reduce the run time, in error clusters with more than 100 voxels (determined through experiments), error voxels of the same clusters are sampled by taking every fifteenth voxel (determined through experiments). The worst-case scenario in mapping occurs when the error voxels are drilled out in different portions of the procedure. However, since the drilling can be performed in one direction from the top occlusal surface towards the pulp floor only, this issue is solved by indexing error voxels.

After mapping the error information with the procedure, the identified portions of the procedure are extracted to analyze the applied force, and the orientation on the instrument. In the absence of a standard amount of force and orientation, the challenge comes in determining whether the applied force and the tool orientation are correct. To get the baseline data, we had an expert perform the procedure three times. For each stage of the procedure, the average of applied force, and the mean orientation of the instrument were collected. The correlator compares force and tool orientation with the expert data for each stage.

The mapping information on each error cluster: the types of errors, their locations on the outcome, the timestamps during the procedure, the differences in applied force, and the orientation of the instrument relative to the expert in x-, y-, z-axes are combined and sent to the feedback generator component. The pseudo-code of the correlator component is shown in Fig. 11.

4. Feedback generator

The correlator component provides information on types and locations of errors in the outcome, and portions of the procedure identified as causes responsible for them. Another challenge is to decide how to effectively convey this information as tutoring feedback. The errors of different types are located in the different regions of the outcome, and the portions of the procedure, which are identified as the origins of the errors, are temporally distributed across the procedure. We hypothesize that video could be an appropriate modality to convey the feedback as it allows the student to easily navigate through the procedure for review and the task-relevant visual aspects of internal anatomy, the errors in space and time can be conveyed through it. Hence, the video-based formative feedback generator was implemented as a modality to provide feedback using the dental VR skill-training simulator.

4. 4.1. Video-based formative feedback generator

The feedback information is provided by replaying the procedure in the simulator while highlighting the error areas within the tooth volume at the identified point of time associated with the incorrect actions determined from the correlator component. The video playback interface consists of four main components: a video control panel, a mode control panel, the simulation panel, and a viewing aspect control panel (Fig. 12).

4.1.1. Video control and mode control panel

The video control panel offers access to several standard buttons including Play, Stop, Skip Forward, and Skip Backward. The Play button toggles into Pause while the video is being played. The Skip Forward and Backward buttons are used to skip to the next/previous error in the video or the beginning of the nearest stage, whichever comes first. They allow the user to quickly and efficiently jump to the point of the error or stage. Fast-forwarding through portions of a procedure that may not contribute to the overall assessment reduces the time needed to complete the playback. Instructors as well can benefit from this feature, which can be used as a supplement in the assessment.

As the video is being played, a video progress bar is highlighted with red, blue, and yellow to denote the overcut, undercut, and perforation errors, respectively (Fig. 13). The color-coded error regions enable users to quickly focus on those portions of the procedure where errors were committed. Two vertical Stage Border bars appear on the progress bar to represent the three stages of the access opening procedure. They serve as reference points to indicate the current stage being played, the time spent in each stage, and the time and the type of errors that occurred in each stage.

The simulation panel (Fig. 14) hosts the video replay of the procedure consisting of the tooth, the handpiece, and the mirror. In the default video replay mode, the original opaque tooth is displayed; however, for a better understanding of feedback, the students can switch to the transparent mode during the playback. In the transparent mode, the error voxels are highlighted according to the type of errors (w.r.t progress bar) as they are being drilled.

The Mode control panel allows the user to switch between three modes: Train, Feedback, and Replay. In the Feedback mode, kinematic comparison graphs (concerning the force applied to the tooth (along x, y, z-axis), the orientation of the driller (along x, y, z-axis) between the trainee and the expert during the video playback. In the Replay mode, the system allows the user to view his/her performance in comparison with video playback of the expert performance (Fig. 15). The teeth in both windows are displayed in the transparent mode to allow the student to view the changes in the tooth internally as it is being drilled.

4.1.2. Viewing aspect control panel

Dentists commonly use axial walls to communicate and therefore, perspectives from the four axial walls (Mesial, Lingual, Distal, and Buccal) are provided to the user. Upon selection, the camera will be rotated to the selected wall, and the user can view how the tooth is being drilled from the selected wall (Fig. 16). Users can also rotate the viewing angle step by step by tilting the walls and turning left/right. As shown in Fig. 17, six- rotation buttons are Buccal Tilt, Lingual Tilt, Mesial Tilt, Distal Tilt, Left and Right. The zoom in/out functions allow the user to have a closer look at what is happening while the tooth is being drilled (Fig. 17).

In the default view, the tooth is positioned with the Buccal wall facing towards the user. As the tooth is being drilled, the Lingual and Mesial walls may be partially blocked from view by the drill and mirror. Therefore, functions are provided to remove the handpiece and the mirror from the playback scene.

5. Evaluationl

We aim to evaluate two main hypotheses:

  • Hypothesis I: Skill training using the simulator with video-based formative feedback is more effective than training using the simulator without feedback.
  • Hypothesis II: Skill training using the simulator with video-based formative feedback is more effective than the traditional training approach.

To evaluate these hypotheses, a pre-test/post-test control group design is used with three groups:

  • Experimental group I (G1): The participants in this group are trained with the VR simulator without feedback.
  • Experimental group II (G2): The participants in this group are trained with the VR simulator with the video-based formative feedback and the overall outcome score obtained from the outcome scoring system.
  • Control group (G3): The participants in this group are trained without the VR simulator in the traditional laboratory setting.

To test Hypothesis I, the learning gain of the training is defined as the difference between the pre-and post-test scores. Hypothesis I is confirmed if the student group trained with the simulator with video-based formative feedback achieves higher learning gain than that of a control group consisting of students trained with the simulator without feedback. The null and alternative hypotheses are:

  • Null Hypothesis (H0): There will be no significant difference in learning gains between the participant group trained using the simulator with the video-based formative feedback system (G2) and the participant group trained using the simulator without feedback (G1).
  • Alternative Hypothesis (HA): The learning gains of the participant group trained using the simulator with a video-based formative feedback system (G2) will be greater than that of the participant group trained using the simulator without feedback (G1).

Regarding Hypothesis II, to confirm that the two training methods are equally effective, we compared whether the learning gains of the participant group trained with the simulator with feedback are equivalent to outcome scores of the participant group trained in the traditional laboratory setting. The null and alternative hypotheses are:

  • Null Hypothesis (H0): There will be no significant difference in learning gains between the participant group trained using the simulator with the video-based formative feedback system (G2) and the participant group trained in the traditional laboratory setting (G3).
  • Alternative Hypothesis (HA): The learning gains of the participant group trained using the simulator with the video-based formative feedback system (G2) will be greater than that of the participant group trained in the traditional laboratory setting (G3).

Ethical approval was obtained from the Institutional Review Boards of Mahidol University and Thammasat University. We recruited thirty dental students at Thammasat University School of Dentistry, Thailand. The inclusion criteria include the students who were in the fifth year at the dental school and have no prior experience with haptic VR simulation. They were not admitted to the study if any of the following criteria were present: left-hand dominant individual; had prior experience with the simulation; or received below 70 percent marks in knowledge assessment of the endodontic access opening. No participant dropped out of the study. The flowchart of participants through the trials is shown in Fig. 18. At the end of the experiment, the authors have optional informal interviews with the participants.

6. Experimental setup

After consenting to participate in the experiment, each student was provided with a plastic typodont mandibular left molar and asked to prepare the access opening for the root canal treatment. The artificial plastic teeth are designed for endodontic training with simulated anatomical pulp cavity and canals and have an x-ray imaging ability. Similar to working with natural teeth, trainees can experience the difference in cutting feel between the enamel and the dentin material. The teeth were acquired from Nissin Dental Products Inc. (http://www.nissin-dental.net/). Examples of the drilled tooth before, during, and after preparation are shown in Fig. 19. We would like to note the difference between the simulated tooth (lower right molar) and the plastic teeth (lower left molar). The lower left molar tooth was used in the evaluation study as it is the only lower molar tooth available in supply and the internal anatomy of the tooth is similar to the tooth used in the simulation. Students were additionally provided with a tungsten carbide bur (3 3 0), a millimeter graduated periodontal probe, a mouth mirror, and a sharp straight dental probe. All teeth were coded anonymously.

Data were collected in separate sessions between control and experimental groups after study hours. In the pre-training session, all participants performed access opening in the laboratory using plastic teeth. During the training session, participants from G1 were trained using the simulator without feedback; participants from G2 were trained using the simulator with formative feedback, and participants from G3 were trained in the traditional laboratory without the VR simulator.

Participants from G1 and G2 were briefly instructed on the use of the simulator, the experiment flow, and the requirements of the access opening. The participants received a verbal explanation about the use of the system from the investigators and familiarized themselves for fifteen minutes with the system interface, but not with the task. Participants from G2 were also informed that they were allowed to stop the video feedback once they felt that they understood the errors and the causes. During this familiarization or warm-up period, each participant was allowed to ask questions and receive further verbal explanations and suggestions from the investigators. After the familiarization, the participants continued in training sessions. During the training stage, participants from G2 received scores on the outcome from the automated outcome scoring system and video-based formative feedback on the performance. They were allowed to navigate the video playback freely and exit before the replay was over (and many did) if they felt that they had understood how and what lead to the resulting performance score.

The primary outcome measure used was the average of the outcome scores of each pre-and post-training session, assessed by a panel of two experts who were blinded to trainee and training status. The standard preparation is the preparation (i) with the straight-line access to the pulp chamber and root canal system without missing the orifice in all walls (Mesial, Distal, Buccal, Lingual and Pulp Floor), (ii) without excessive removal of tooth mass, and (iii) without perforation. Based on this, all tooth surfaces (Mesial, Distal, Buccal, Lingual, and Pulp Floor) were evaluated and graded using evaluation parameters – (i) the straight-line access to the pulp chamber and root canal system without missing the orifice, and (ii) smoothness of the preparations (Errors undercut, overcut, and perforation are penalized based on the severity)– as assessment criteria. The outcome score ranges from 1 to 100, with 70 being the clinically acceptable and passing score. The outcome score was considered as the primary dependent variable representing the success in learning outcome while the scores on axial walls and the pulp chamber floor were considered for detailed analysis of performance.

7. Results

The two experts evaluated the outcomes from the control and experimental groups in pre-and post-training steps. The normality of the variables was confirmed using the Kolmogorov and Smirnov test. Since the outcome scores in this study were normally distributed, we computed the intraclass correlation coefficient (ICC) [30] to determine the degree of agreement between the scores of the two experts. ICC values range between 0.0 and 1.0, with the highest value indicating strong agreement between the scores given to each tooth by the raters. The high ICC values shown in Table 1 indicate strong inter-rater agreement in all categories (the axial walls, the floor, and the overall scores). All the coefficients of ICC were significant at p = 0.05. The highest ICC (0.99) is observed in the overall score while the lowest (0.91) is found in the floor scores.

The descriptive statistics of pre-and post-training scores in all groups are summarized in Table 2. The mean overall scores before training range between 61.30 and 65.80, while the means after training range between 66.30 and 91.6. The overall post-training scores showed a marked decrease in standard deviation compared to the pre-training scores in G2 (8.07 from 15.52) and G3 (7.70 from 15.71), indicating the convergence in performance of these two groups. The participant group trained using the simulator with feedback achieved higher mean post-training overall outcome scores (G2 Post-Mean = 91.6) than the control group trained with the simulator without feedback (G1 Post-Mean = 66.3), and the group trained in the traditional laboratory setting (G3 Post-Mean = 72.20).

Table 3 shows the mean learning gains for the three groups in the four-axial wall, the floor, and overall. The learning gain of each student is computed as the difference between the post-training and pre-training scores. Independent samples t-tests were used to compare the learning gains among all three groups. We found that the student group trained with the simulator with feedback (G2) achieved statistically significantly the highest learning gain in terms of overall score (mean 30.30 ± standard deviation 17.5) at the end of the training session. The statistically significant higher gain compared to group G1 trained with the simulator without feedback (mean 0.5 ± standard deviation 11.375), t (18) = − 4.523, p = 0.000, confirms our hypothesis I that skill training using the simulator with video-based formative feedback is more effective than training using the simulator without feedback. Similarly, experimental group G2 had statistically significantly higher learning gain at the end of the training session compared to the control group G3 (mean 8.5 ± standard deviation 14.152), t (18) = −3.068, p = 0.007. This confirms our hypothesis II that skill training using the simulator with video-based formative feedback is more effective than the traditional training approach.

To have a better understanding of the difference in performance between groups, we analyzed the learning gains in the axial walls and the pulp floor of each group. Negative learning gains were observed in G1 for Buccal and Lingual walls, as the scores of participants from G1 significantly dropped from pre-training scores in these two walls (Table. 3). In contrast, G2 and G3 had positive learning gains in Buccal, and Lingual, with higher learning gains observed in G2. One-way analysis with Tukey posthoc tests revealed that the mean learning gain of G2 is significantly higher in the Mesial and Distal wall scores than that of G1 (Mesial: p = 0.04, Distal = 0.00) and G3 (Distal = 0.02) in those walls. Experts highlighted the fact that even though the simulator did not include the dental probe tool, the positive learning gains indicate that the participants from G2 may be gaining benefits from observing errors visualized in axial walls in the video-playback.

We next examine within-group individual learning gains. A paired t-test was used to compare the difference between the means of pre-and post-training scores. At p < 0.05, significant differences are found between pre-and post- training scores for all categories in the experimental group G2. In contrast, a significant difference is found for G3 only in the distal wall and in no learning gains for G1.

A further important question is how the initial skill level affects learning gains. From Fig. 20 we can see that in group G2 for low initial scores the learning gains are high, but for high initial scores, there is little improvement. The same trend holds for group G3, but the effect of initial skill level is less pronounced. For group G1 we see little effect, if any.

8. Discussion

Dental students acquire pre-clinical knowledge from a range of media including didactic lectures, seminars, and online learning, and reading. In translating knowledge to skills, dental students practice using extracted human teeth, artificial teeth (and jaws) mounted in phantom heads, and computed-based training simulators. Artificial teeth allow instructors to improve a student’s hand-eye coordination, indirect vision, and dexterity, but tactile sensation is difficult to explain verbally [44]. Other drawbacks include a lack of anatomical structure and high cost. Extracted teeth have higher fidelity of physical properties than artificial teeth, however, standardization of training procedures is often problematic due to anatomical and pathological variations.

With traditional phantom head simulator training practice, students should ideally receive assessment and feedback on each stage of their work to move on to the next stage of the procedure. However, tutors are often only able to inspect the outcome of each student due to time constraints and high student-to-tutor ratio [18], [44], [61]. By only examining the result of the pre-clinical training task, instructors can rarely assess the actual procedure followed by every student to achieve the desired outcome, and the feedback can be subjective to the experience and opinion of the instructors [40]. In this setting, when it is given, the feedback is often nonspecific, making it ineffective in providing learners with concrete strategies on how to improve.

In recent years computer-based simulators have been widely adopted into the dental curriculum [10], [13], [62]. The operator of a computer-based simulator is usually presented with a 3D target area that they are instructed to remove using a dental handpiece. Typically, feedback is generated using a combination of the amount of the target shape removed, the damage done to the area outside of the target, and the time taken to complete the task. Despite having several limitations, this shape agreement approach has been widely adopted in computer-based simulators [53], [55]. But knowing the percentage or volume of material removed inside or outside of a target area might not help the endodontic trainee who is practicing to improve skills. Since not all the materials removed are equally important, the trainee should be informed about which areas the material has been removed from and how critical those areas are.

Metrics based on kinematic data from the user’s movement and force exertion have also commonly been used as the basis for comparison with an expert’s performance on the same exercise [52]. Rhienmora and colleagues [41] presented and evaluated such a feedback system using a haptic dental simulator. Comparing a student’s performance with an expert in this way is using more factors than the shape agreement method, however, the information is limited to a particular exercise for a particular tooth only. Although it may not immediately correlate with the internalization of that skill so it can be transferable to other contexts, still it is useful for the trainee to learn how the expert would perform in a given scenario.

Another commonly used metric in computer-based dental simulators is task completion time or task time [4], [20], [57]. While learning and developing a skill, receiving feedback on how much time was taken may not be particularly useful. It may be true that an expert can perform a procedure more quickly than a novice, but providing this metric simply informs the novice of this fact without offering any guidance on how to achieve mastery. Additionally, it has been shown that introducing time pressures can negatively impact a novice’s performance and impede their ability to concentrate on the factors that actually would lead to improved performance [9].

Central to effective learning in simulation-based skill training is the role of feedback on a learner’s performance [8], [29], [33]. The formative feedback in our study is constructed by forming a linkage between information about the outcome of the performance, which is known as knowledge of result (KR), and information about the quality of performance and movement characteristics, known as knowledge of performance (KP). The availability of KR feedback during simulated practice has been identified as one of the most important factors leading to differences in motor learning [19], [46], [48]. Provision of formative feedback from the simulator was found to result in significant performance improvements relative to the training using the simulator without feedback (G1) and training in the conventional setting (G3). Learning gains were particularly strong for trainees with low pre-training scores in G2. Gains were lower for similar students in G3 and were only moderate in G1. This shows the benefit of the simulator with feedback over traditional training and the simulator without feedback for students with low skill levels. It suggests the importance of high-quality formative feedback, particularly for students in the early stages of skill development. In [50], the authors showed that even when the novices were provided with a simple one-time summative feedback on the outcome score, participants trained with the haptic VR simulator and conventional phantom head had equivalent effects on minimizing procedural errors in endodontic access cavity preparation. They also reported that the participants trained with the VR simulator tended to remove less tooth mass. Positive learning gains from G2 and G3 in our study indicate that the participants trained with the haptic VR simulator with formative feedback and conventional phantom head had similar effects on minimizing procedural errors in endodontic access cavity preparation. Although the tooth mass was not measured in this study, with the positive learning gains, we expected the participants in our study achieved the same effect.

Feedback for the development of psychomotor skills can be classified into immediate and terminal, where immediate feedback occurs immediately after action and terminal feedback occurs after procedure completion. Both immediate and terminal feedback have different strengths and are indispensable for skill training. Frequent immediate feedback is considered appropriate for early novices who are not familiar with the procedure (or the tools) at the cost of potentially interrupting the students, whereas terminal feedback is suitable for users who already have substantial knowledge about the procedure, including how to perform the procedure, like the participants in our study. A clear benefit of immediate feedback in a traditional training environment is its ability to provide a link between action and outcome, which may be lost in terminal feedback. Our system occupies an interesting space between the two approaches. By providing feedback at the end of the procedure, we avoid interrupting the flow of work of the student. At the same time, by replaying the student actions and highlighting errors made, we retain the linkage between action and outcome important in learning and refining psychomotor skills. Also, we permit students to re-try portions of the procedure they choose. This formative informational property of the feedback [45] directs the learner in terms of how to correct the error on the next trial. The differences in the learning gains between the two groups (G2 and G3) observed in our study indicates the potential benefits of the terminal formative feedback in skill training in relation to the traditional setting.

Procedure playback is commonly used as a terminal feedback modality in VR simulators. According to a survey of existing VR dental simulators for skill training by Wang et al. (D. [56], the ForssLund simulator (Forsslund [17], the HapTel simulator [53], and the Simodont simulator [6] have replay features which allow the student or instructor to watch in full replay mode upon completion of a procedure. All these existing systems provide simple playback of the procedure carried out using the simulator without any augmentation. In contrast, our approach augments the replay with information about the errors committed. With our simulator playback feedback system, the trainee can deconstruct the actions and errors that unfold during the procedure, and identify the information necessary to improve in subsequent practice. A few examples include reviewing how the drill/handpiece was in close contact with critical regions in the operating area, the amount of force used on the handpiece, and how it could affect the outcome, the speed, and direction in which to move the handpiece to remediate the errors. Instructors often use debriefing to guide the trainees to explore and understand the relationships among events, actions, thought and feeling processes, as well as performance outcomes of the simulation [23]. This video-based formative feedback could assist the endodontic experts in debriefing with detailed feedback on procedural aspects which are usually excluded from post-procedure debriefing.

Practice in simulation-based learning environments may improve student decision-making and error management opportunities by providing a structured experience where errors are explicitly characterized and used for training and feedback [38]. White et al. [60] noted that trainees’ knowledge is increased by making and learning from errors. Our system is designed to give students the freedom and autonomy to commit errors and then in retrospective feedback shows them where errors occurred and provide information concerning the causes of the errors. Trainees can develop an understanding of how their actions lead to correct or incorrect results which are considered to be highly effective feedback for motor skill development [59].

Our feedback prototype was developed for the access opening stage of endodontic root canal treatment, which is one of the most challenging areas of dental surgery. Creating a proper access opening is critical to the success of the later stage involving instrumentation of the root canal system. The small pulp chamber is encapsulated deep inside the tooth so that working in the pulp area demands fine motor skills and experience. In the access opening stage of an endodontic root canal procedure, dental students tend to have difficulties in adequately deroofing the chamber, reaching the pulp chamber, and locating the orifices [32]. Overcutting errors may result in excessive loss of the tooth structure and subsequently lead to brittle and fragile teeth with decreased fracture strength against loads [11]. Undercutting errors can lead to missed root canals to be treated or to instrument breakage if the access to the canals is not adequately expanded and extended. Various studies have demonstrated that such procedural accidents have a negative effect on the prognosis of the overall treatment outcome [3], [22], [27] and correction of such errors is difficult, if not impossible [15], [25].

The shape of the access opening is dictated by the pulp morphology. A meticulous study of pulp morphology is essential to design any therapeutic intervention plan [37], [54]. In a study by [15], the authors concluded that negligence, the lack of planning, and unfamiliarity with the internal anatomy contribute significantly to the failure of root canal treatment. To date, cone-beam computed tomography (CBCT) and micro-computed tomography have been used in conjunction with digital radiography images for visualization, measuring, quantitative or qualitative analysis, three-dimensional assessment, and design in endodontic treatment planning. Endodontic treatment planning can be further improved through the use of training simulators like the one presented in this study. With the CBCT of the patient’s tooth, the trainees could plan the treatment as well as repeatedly rehearse to achieve the optimal straight-line access outcome. Each plan can be judiciously analyzed to keep the procedural errors at a minimum. With the 3D video playback, the sound restorative margins and the possibility of retention of natural dentin, also the amount of remaining tooth structure can be visually confirmed for each treatment plan. Undergraduates novices who reportedly have low technical proficiency [17] and trainees who did not feel to perform the endodontic procedures [65] could be benefitted by rehearsing the plans with objective formative feedback. The access opening and cavity preparation is an important step of root canal treatment as all other factors precede this step. By keeping the procedural errors at a minimum in this step, the better the prognosis of the treatment can be expected.

9. Limitations and future work

In this study, only the three most common types of errors (undercutting, overcutting, perforation) associated with the access opening procedure stage from root canal treatment are taken into consideration. Details are provided in Section 3.2. Errors can be further analyzed at a more detailed level, for example, we could distinguish between lateral and vertical perforation errors. Similarly, the undercut error could be separated from incomplete error (unfinished task outcome) by thresholding the drilling region below the minimum template.

A limitation of the evaluation is that there are two forms of feedback in the experimental group: the outcome score from the automated outcome scoring system and the video-based formative feedback. In designing feedback systems, it would be good to know to what extent these different aspects contribute to improvements separately. This could be addressed by adding another experiment group and training the participants with each specific type of feedback.

The system’s feedback component could be extended in many ways. The system could be extended to have a feature to save the playback as a video, to enable students and instructors to keep records of student performance, and use it to monitor their progress. Instructors could also use it to review student skills and performance without having to be present during the training session. In determining the factors that contributed to errors, we focused only on the instrument during the procedure. However, the orientation variables associated with the mirror could indicate whether the trainee properly manipulates it whenever the indirect view of the operating tooth is needed. Besides, this study could be extended by distinguishing the sources of errors. Each error could be analyzed to determine whether it is caused by a lack of psychomotor ability or lack of relevant knowledge or a combination of both. This information could be instrumental in deriving directive feedback on correcting errors, inappropriate actions, or misconceptions. We plan to include the above-mentioned features in future work.

10. Conclusion

Simulation-based surgical skill training was largely driven by the tenet that simulators facilitate deliberate practice without risking the patient. However, assessment and feedback are as yet underutilized. Our formative feedback system provides an objective feedback mechanism and could be incorporated into formal skills training curricula. We would like to emphasize that virtual simulators cannot replace the experts during training but rather complement experts in the training process. While simulators are the perfect platform for deliberate practice, they can never replicate fully the clinical experience of experts nor their ability to motivate students. As simulators provide assessment and feedback for each practice session, experts can focus on qualitative feedback aspects of skill training. When both the expert and the simulator actively engage in the training process, the benefits are multifold. Expert’s time and workload could significantly reduce with the addition of VR simulators equipped with assessment and feedback features.

CRediT authorship contribution statement

Myat Su Yin: Conceptualization, Methodology, Writing – original draft, Writing – review & editing, Visualization, Software, Resources, Validation. Peter Haddawy: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Siriwan Suebnukarn: Conceptualization, Resources, Validation. Farin Kulapichitr: Visualization, Software. Phattanapon Rhienmora: Visualization, Software. Varistha Jatuwat: Visualization, Software. Nuttanun Uthaipattanacheep: Visualization, Software.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was partially supported through a fellowship from the Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany to Su Yin for collaborative work with the University of Bremen and Santander BISIP Scholarship to Kulapichitr.

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10 Oct
info

Augmented reality (AR) and virtual reality (VR) applied in dentistry

Copyright 2018, Kaohsiung Medical University. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

 

 

Abstract

Abstract : The OSCE is a reliable evaluation method to estimate the preclinical examination of dental students. The most ideal assessment for OSCE is used the augmented reality simulator to evaluate. This literature review investigated a recently developed in virtual reality (VR) and augmented reality (AR) starting of the dental history to the progress of the dental skill. As result of the lacking of technology, it needs to depend on other device increasing the success rate and decreasing the risk of the surgery. The development of tracking unit changed the surgical and educational way. Clinical surgery is based on mature education. VR and AR simultaneously affected the skill of the training lesson and navigation system. Widely, the VR and AR not only applied in the dental training lesson and surgery, but also improved all field in our life.

KEYWORDS:  OSCE; Dental simulator; Augmented reality; Virtual reality; Dentistry


INTRODUCTION

With the increase in the elderly population and the economic growth, the concept of oral health gradually increased, and dental and dental health care issues are increasingly important. Due to the high incidence and prevalence of today’s global oral diseases, the global market for oral medical equipment in 2016 was $ 23.99 billion up to 4.0% from 2015, and the market was expected to reach $ 29.09 billion by 2020.2015e2020 growth rate was up to 4.7%, coupled with the incidence of the poor than other socio-economic groups. The oral disease has become an important public health problem, and promote the global oral medical market continues to grow.

In addition, according to World Health Organization statistics show that more than 60% of school-age children worldwide and nearly 100% of adults have dental caries status, and 35e44 years of adult population, nearly 20% suffering from severe teeth disease, follow-up will lead to the possibility of missing teeth. As for the 65e74 year old population, the total tooth loss rate is as high as nearly 30% [1]. With the increase in the number of elderly population and the increasingly aging society, coupled with the majority of elderly people in the treatment rate is generally low. It will lead to long-term sustained increase in oral medical needs.

Nowadays, there are a lot of skills about the progresses in the computer-based technologies such as augmented reality (AR) and virtual reality (VR). In the two kinds of reality, AR is the first application began to widely use. AR, in which 3D virtual objects are integrated into a 3D real environment in real time. AR is to “virtualize” the virtual image into the real space, creating a completely virtual space around the user’s eyes to replace the real space. To make the users see a world which have a real environment and generated by the computer graphics over a real scene [2]. And the VR offered the users a real, inside virtual 3D model [3e5]. According to the display, to build a three dimensional, seemingly true virtual world in the user’s eyes. Recently, VR also designed head mount display with special glasses to cover the user’s surrounding vision to achieve the interaction situation.

With the increasing demand for dental implants, the dentist-related faculties or post-graduated year (PGY) professional competencies, clinical training and experience accumulation are more important, and these technologies are directly reflected in the school’s education. Through the complete education and training with realistic exercises and assessments, in order to training a dentist. Therefore, whether it is on the education side or the clinical side, increasingly mature technology development developed by the auxiliary products will become more and more important role in the surgery and education training process.

The history of dentistry

The history of dentistry is almost as long as the history of human civilization. The progress of science and technology, the application of technology used in the dental became more and more mature. From the initial, using pliers to remove the tooth, wire to lock loose teeth, and the dental appliance and dental bridge. To the beginning of the 17th and 18th century, using the tooth filling [6], gradually developed to the initial bone as a denture concept to replace the loose teeth. To use the tooth sets of metal wire and fixed appliance techniques to correct the tooth position [7]. Until today, dental expertise is currently used to prevent and treat common oral diseases, namely dental caries and periodontal disease, and the field also includes common repair, extraction, implant, root canal therapy and calculus removal.

Nowadays, dentists in the United States and European countries must pass both written and technical examinations before obtaining a license. Dentistry in Japan and China also has fully implemented the above mentioned examination policies. In view of this, enough practice, professional knowledge in medical and dental colleges. The better way of learning is without question a developing trend for global dental education. Learning educational equipment and method built around such technology will be a must-have for dental universities around the world.

Informative technological advances in dentistry

With the advanced development of Information Technology (IT), dental solutions lead by computer and internet technologies have made significant progress all over the world. Digital dental solutions will be the trend for the professional dental field in the future. The rapid development of digital dental solutions has been applied in both the clinical dental field as well as the dental education field. This trend will gradually challenge both traditional dental clinical practices and dental education learning methods.

With the medical image of the increasingly mature can help physicians to identify the patient’s affected area and to make a different cure. The new technology which assisted the doctor has gradually been mature. The Image-guided therapy (IGT) [8], [9] and Image-guided interventions (IGI) technology development [10], [11], [12], [13], [14], [15], the image recognition and location of tracking system [16], coupled with computer computing [17], combined computed tomography, position tracker, display and PC to achieve tracking location and surgical instruments immediately. By calculating the position of the medical images and surgical tools [13], to provide more accurate accuracy in the surgical position or learning lesson Recently, the current of article about the nerve surgery published in PubMed more than 1400 [18]. In the nerve surgery also combined with the above technology to achieve the effect of surgical real-time. And because good image clarity will affect the overall system of precision [19], medical imaging such as CT technology advances, with a good tracking system also reduces the risk of surgery and mistakes [12].

Educational applications of dentistry

Dr. John M. Harris opened the world’s first dental school in Bridge, and helped establish the dental establishment as a health career [13]. The school was opened on February 21, 1828 and is now the Harris Dental Museum [10]. Studies have shown that graduates who graduated from different countries [11] or different dental schools may have different clinical decisions for the same clinical condition [12]. This means that in each country or school has its own teaching and scoring methods. The primary issue about VR and AR that to achieve the standard score way can be standardized to facilitate student learning and practicing. Now there are several systems and devices using VR and AR [Table 1] [20], through the tracking system to achieve handpiece and the screen synchronization, the characteristics of the system equipment and comparison as follows: Which DentSim™ is a complete system that incorporates VR and AR with its system included ergonomic postures, instant feedback, exam simulation; direct transfer of data to programmer and the system can be used in the campus [20], [21]. According to the system with VR and AR not only integrate systems for learning and teaching from an organizational perspective but also training skills and improve the hand-eye coordinate [20], [22]. The results that system can improve the users correct the posture and skills. And some prove showed that lots of the information technology about VR and AR can train the users and make them familiar with the system, skill and the lesson [23], [24]. A system with VR and AR would become an educational tool to make the students learn by themselves and some reports proved that it can decrease faculty time by fivefold when compared to traditional preclinical teaching methods [25], [26], [27].

A complete VR and AR system in the hardware, there are the teeth model, handpiece with the motor, different brand of the burs and the air and water in-and-out. In the software, it included the simple registration to make the position of the instruments in the system and the real-time tracking with correct accuracy. And it could offer different lessons to let the students learn. A different lesson included cavity preparation, crown and bridge and access preparation. According to different lessons may make the students and PGY practice and be familiar with different symptoms.

Which another system, CDS-100, designed by the EPED Inc. Computerized Dental Simulators is dental training systems applied with new technology such as VR and AR. This technology provides the best computer training system for dental students and PGY dentists in need of self-training. Some advantages are as followed:Optical positioning system provides 3D real-time accurate feedback of optimal teeth’s angle, depth, and abundant software lessons (Operative Dentistry, Endodontics, Crown and Bridge and Pedodontics) provided students easy self-learning and practicing with digital guide and simulations. Courses & Lessons can be customized and designed as well as upgraded for specific projects. Abundant accessories included the system, such as tooth model, teeth, different brand burs, manikin chair, shadow-less lamp and the posture evaluation system. And the brand of the tooth, tooth model also can be used in the system. According to another system, the Implant Real-time Imaging System (IRIS), the doctors also can be offered the complete education and experience about the implantology and clinical treatment. Objective Structured Clinical Examination (OSCE) incorporated into the operative dentistry software curriculum. With a Computerized-Objective Evaluation System, teachers can set up and highlight the score percentage with easy way. Teachers can evaluate students’ learning status through digital reports to further strengthen students’ learning objectives. Evaluation reports with figures and descriptions offer easy self-learning and comparison studies to improve clinical practice and precision. And according the record, students can review the progress and find the mistake to improve the skills. In the lesson, broadcasting function provides teachers a way to easily present and demonstrate to observing students via remote connect. Broadcasts can be done in real-time or at the student’s convenience. Broadcast and playback features provide an effective solution to solve the imbalance ratio between teachers and students, as well as provide an educational tool for college assessment and improvement rankings. Through the digital dental simulators and clinical environment, it is easy for students’ to self-practice, allowing them to gain crucial clinical experience and precision.

Another system, Moog Simodont Dental Trainer, is also a training system for dental schools. The system can help students progress faster and realize the progress, also can offer the teacher plan students work efficiently and track the students’ progress. The system target is that training the students’ skill such as removing tooth decay, preparing crowns with different dental burrs. Customized cases can be created and students’ work traced and evaluated by software and teachers. Moog Simodont Dental Trainer combines Moog’s expertise in haptic technology and ACTA’s (Academic Center for Dentistry in Amsterdam) experience in dental education to help students practice more efficiently and learn faster.

These three systems have their features, like Moog, according to competence oriented with immediate feedback, no water lines, plastic teeth and no burr consumption. And such as CDS-100 and DentSim™, by the visible device, the users can see and use the real handpiece and teeth to learn and compare the visual and the reality teeth. Such like CDS-100 has a OSCE and objective evaluation (Fig. 1). The real-time observation in the visual and real teeth, customized and abundant lesson, high accuracy and evaluation are a good and standardized tool for the all students and PGY.

Clinical application of dentistry

With the development of dental technology, the system combined the surgical instruments, tracking system, medical images and computer became to the real-time navigation technology [28]. The tracking system has a different component to composed, and according to the work of component distinguish different types, such as electromagnetic and optical tracking system [29], [30], [31]. At first, the navigation system is not mature enough to exceed the stereolithographic surgical guide of the accuracy. The method of the surgical guide expressed high precision than the navigation system [32]. The position or place of the registration is important for the accuracy of the navigation system. The wrong place would produce the larger error. So according to the intended use of the navigation system, there are the different ways of registration [33]. Although the navigation played an important role but also the high quality medical images would make the clear information in the patient. High quality medical images would increase the rate of the surgery [19]. There are some reasons to affect the accuracy of the navigation system, such as integrated component, medical images accuracy, surgical tracking unit, registration accuracy and targeting. Many reasons because of the progress of the visual reality, they became to be overcome [28]. Currently, the navigation system became mature, the acculturate accuracy expressed better expression than the surgical guide [34]. There is a mature system, IGI (DenX Advanced Dental system), showed good expression in the dental surgery [28], [35], [36]. It played an important role in the dental implant technology. Especially, it is necessary to ensure the success rate and decrease the risk of dental implants. Traditional dental implants must be local anesthesia, and the scalpel flap treatment. It needs to wait for 3–4 months after surgery until the wound healing in order to install dentures, and because the dental inconvenient, resulting in occlusion dysfunction occurs. After the navigation system and the advanced development in the surgical instruments, it can be through the pre-surgical plan to adjust and plan a precise placement of the implant, not only greatly shorten the operation time because of the small wound or even the flapless surgery [35], but also accurately predict and avoid the nerve. By the way, there are some cases about investigating the patient using the navigation system, the situation of the wound and the implant position. As the report, after one year, the situation of the recovery is good [37]. Another system, Iris-100 (Implant Real-time Imaging system), designed by the EPED Inc., also can achieve the same function like IGI. According to CT images, Iris-100 can make the images dynamic to control the situation in real-time in the implant region. The Iris-100 system monitored the drills’ depth and angle to achieve the best effect in the lowest risk. By the CT images, doctors can do the pre-surgical planning which can lead the drills’ position in the surgery. Abundant customized hand piece and drills, tissue punch also combined and applied in the navigation to achieve the special surgical case and flapless surgery. Navigation system is the best assistant tool, by the process of guiding system, you can clearly distinguish the location of the implant, and the direction and depth. To reduce the pain of patients due to surgery, although the computer navigation may not be 100% accurate, there is need to professional physicians on-site real-time monitoring, through a wealth of experience, and the risk of the surgery would decrease.

VR and AR combined the tracking system in real-time in the surgery

The skill of the visual reality became mature, and more and more VR and AR showed on the educational and surgical field. The development of reality devices allow the user to combine the medical information, medical data and incorporate these data visualized. It can provide more clear information and make the users improve safety and lower risk [38]. Although the visual reality and augmented reality in the dental field is not enough common, in other field developed much better, such as the neurosurgery and cranial surgery [39]. The users can use the Head Mount Display (HMD) display to see the medical information and images combined to the surgery [40]. And it can decrease the surgical risk much better than the common visual reality.

Summary

The AR or VR simulators with direct feedback and objective evaluation function may become an important tool in the future of dental OSCE. The development of the VR or AR is a good tool for our society. Not only applied to the education, but also developed in the clinical treatment. We believe that in the future the VR and AR training and lesson can spread and apply to the every department of dentistry to make the student and PGY train their skill by themselves. And because of the complete education and training, in the surgery would decrease the risk and create the safety surgical environment. And in the surgery, the visible can combined the accurate medical images, tracking system, targeting, registration and computed even the HMD system or AR, to help the physicians execute the surgery. Depending on the physicians’ experience and the complete hardware and software, it would build a trustable relation between the patients and doctors.

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15 Aug
info

Virtual Reality Relaxation to Decrease Dental Anxiety: Immediate Effect Randomized Clinical Trial

Abstract

Introduction:

Dental anxiety is common and causes symptomatic use of oral health services.

Objectives: 

The aim was to study if a short-term virtual reality intervention reduced preoperative dental anxiety.

Methods:

A randomized controlled single-center trial was conducted with 2 parallel arms in a public oral health care unit: virtual reality relaxation (VRR) and treatment as usual (TAU).

The VRR group received a 1- to 3.5-min 360° immersion video of a peaceful virtual landscape with audio features and sound supporting the experience. TAU groups remained seated for 3 min. Of the powered sample of 280 participants, 255 consented and had complete data. Total and secondary sex-specific mixed effects linear regression models were completed for posttest dental anxiety (Modified Dental Anxiety Scale [MDAS] total score) and its 2 factors (anticipatory and treatment-related dental anxiety) adjusted for baseline (pretest) MDAS total and factor scores and age, taking

into account the effect of blocking.

Results:

Total and anticipatory dental anxiety decreased more in the VRR group than the TAU group (β = −0.75, P < .001, for MDAS total score; β = −0.43, P < .001, for anticipatory anxiety score) in patients of a primary dental care clinic. In women, dental anxiety decreased more in VRR than TAU for total MDAS score (β = −1.08, P < .001) and treatment-related dental anxiety (β = −0.597, P = .011). Anticipatory dental anxiety decreased more in VRR than TAU in both men (β = −0.217, P < .026) and women (β = −0.498, P < .001).

Conclusion:

Short application of VRR is both feasible and effective to reduce preoperative dental anxiety in public dental care settings (ClinicalTrials.gov NCT03993080).

Knowledge Transfer Statement:

Dental anxiety, which is a common problem, can be reduced with short application of virtual reality relaxation applied preoperatively in the waiting room. Findings of this study indicate that it is a feasible and effective procedure to help patients with dental anxiety in normal public dental care settings.

Keywords

dental fear, clinical studies/trials, relaxation technics, virtual reality immersion, dental care, public sector


INTRODUCTION

One-third of Finnish adults are anxious of dental treatment to some degree, women more often than men. A tenth are very anxious. The prevalence of dental anxiety has remained stable over the past 10 y (Lahti et al. 2007; Liinavuori et al. 2016). These statistics are similar in other countries (Hägglin et al. 1999; Maggirias and Locker 2002; Thomson et al. 2009; Armfield 2010; Hill et al. 2013; Carlsson et al. 2015). People with extreme dental anxiety are more likely to avoid or delay treatment (Pohjola et al. 2007; Thomson et al. 2010; Åstrøm et al. 2011; Hakeberg and Wide Boman 2017; Liinavuori et al. 2019), Finnish men more often than women (Liinavuori et al. 2019).

Dental anxiety may be managed by psychotherapeutic interventions, which enable patients to feel more comfortable when receiving the treatment and which help those patients not visiting the dentist due to a high fear to attend the treatment. These interventions include relaxation, distraction, exposure, and other forms of cognitive behavioral therapy (Armfield and Heaton 2013; Gordon et al. 2013; Wide Boman et al. 2013; Craske et al. 2014). Of these, relaxation and distraction are mostly used during dental treatment, whereas exposure therapy, including inhibitory learning, and other forms of cognitive behavioral therapy might be needed before the dental treatment (Armfield and Heaton 2013; Craske et al. 2014). While some of these interventions may be conducted by a dentist, others require support from psychologists (Armfield and Heaton 2013; Wide Boman et al. 2013). Several treatment visits are usually needed to manage dental anxiety, especially for those with extreme dental anxiety; however, a single appointment to reduce dental anxiety has also shown some success (Armfield and Heaton 2013; Gordon et al. 2013; Wide Boman et al. 2013). Based on this research evidence, a brief patient-centered intervention is needed that may be routinely incorporated into daily practice in primary dental care. New technologies have been developed, such as computer-assisted cognitive behavioral therapy, which has shown some potential (Rooksby et al. 2015; Tellez et al. 2015). Technologies based on virtual reality have also been developed for managing dental anxiety. A systematic review concluded that they have potential, though more rigorous studies are needed (Gujjar et al. 2019a). Many of them are based on distraction during normal or simulated treatment or exposure before treatment and used, for example, natural scenery, games, or information on treatment (Frere et al. 2001; Asl Aminabadi et al. 2012; Tanja-Dijkstra et al. 2014; Kazancioglu et al. 2015; Padrino-Barrios et al. 2015; Atzori et al. 2018; Niharika et al. 2018; Shetty et al. 2019), while others are based on psychologist-delivered cognitive behavioral therapy (Raghav et al. 2016; Gujjar et al. 2017; Gujjar et al. 2019b). Short virtual reality–based interventions have shown particular promise in reducing preoperative or anticipatory anxiety in secondary care (Ganry et al. 2018). We are unaware, however, of short virtual reality–based relaxation being applied in primary dental care preoperatively.

Therefore, our research question is as follows: Can a short virtual reality–based intervention applied preoperatively be effective in reducing patients’ anticipatory and treatment-related dental anxiety for those attending primary dental care? The aim is to apply short-term virtual reality relaxation (VRR) to examine if it is effective in reducing anticipatory and treatment-related dental anxiety in primary dental care through a randomized controlled trial (RCT) design.

Methods

Design

A randomized controlled single-center trial was conducted with 2 parallel arms: VRR and treatment as usual (TAU). Groups were randomized, following consent, with an allocation ratio of 1:1. No changes were made to methods after trial commencement.

Participants

Adult patients (≥18 y) who attended for dental treatment (basic, special, or emergency dental care; general anesthesia, x-ray), consented, and were able to complete the Finnish questionnaire without assistance were eligible for the study.

The study was conducted in the public Oral Health Care Unit of the Kalasatama Health and Welfare Center of the City of Helsinki, Finland. Patient recruitment and running the on-site research activities, such as administering the questionnaires and instructing the VRR group in the use of appliances, were conducted by 13 students from the Haaga-Helia University of Applied Sciences and Laurea University of Applied Sciences. Students were trained for this study by the lead clinician (S.L.) on-site to ensure uniformity of information provided to participants.

Patients were approached in 1 of the 2 arrival halls where they entered the Oral Health Care Unit. Patients were inquired if they had 15 min before their scheduled dental appointment to allow participation in the study. If the patients had the time and volunteered, they were told the nature of the study and given an information leaflet describing it and the possibility to win a movie ticket or xylitol products in a lottery after participation. If the patient consented, she or he was then randomized into 1 of the 2 groups.

Interventions

Interventions were conducted in similar settings in small alcoves with a seat and a table. The participants in the TAU group remained seated in the alcove for 3 min. Their experience of sitting in the alcove for 3 min was identical to that of the VRR group but without the VRR intervention. They were able to use their mobile phones if they so wished.

In the VRR group, participants chose 1 of the 5 videos (1 to 3.5 min). Still pictures of each video are provided in the Appendix. The application by MelloVR presented these videos. When the application was launched, clear instructions were displayed on the screen regarding next steps. These included basic instructions on how to select a video by turning one’s head toward a specific video via the so-called gaze selection method without manual controllers. The 360° videos (resolution range, 4,096 × 2,010 to 5,120 × 2,560) immersed the participants in a peaceful virtual landscape (beach, waterfall, underwater, space float, paddling). Videos were played with a Samsung Gear VR headset and a Samsung Galaxy S7 mobile phone (attached to the virtual headset) for the MelloVR application, with a total weight of approximately 500 g. A disposable mask was used with the headset for hygienic purposes.

Audio features and sound supported the relaxation experience. The musical ambient track was the same for all video choices. The file format is AAC with 320-kbps quality playing at 48 kHz. It has a tempo of 120 bpm (beats per minute) and fades in smoothly within 10 s. The musical instrumentation consists of a smooth synth pad, soft kick drum, and occasional bass and bell notes. White noise can be heard on top of the track, which listeners might find relaxing, particularly people with tinnitus. The synth pad looped the same harmony throughout the musical track, and the bass supports it. The bell instrument can be heard a few times, but no specific theme is recognized. This is typical of musical productions that are not meant to raise significant attention. The sound was played with on-ear headphones by Pioneer (model SE-M521) to exclude noise. The picture could be adjusted to suit the user’s eyesight by using the scroll on top of the glasses, and the audio volume could be set accordingly with a control on the side of the glasses.

Acceptability and feasibility of the VRR application were pilot tested prior to the RCT in 55 primary health care and social welfare clients of the Kalasatama Health and Welfare Center. Students who later recruited participants in the RCT invited volunteering clients to try a relaxing virtual reality experience. The virtual reality content and the devices were similar to those in the study. Volunteers’ perceptions were assessed after the virtual reality experience. Of the pilot participants, 98% found the experience relaxing; 87% would like to use it during a potentially anxiety-provoking treatment procedure; and 80% would recommend it to friends. Minor harmful effects, such as feelings of dizziness or nausea, were reported by <4%.

Outcomes

The main outcome measure, dental anxiety, was assessed with the validated Finnish version of the Modified Dental Anxiety Scale (MDAS) before and immediately after the intervention (Humphris et al. 2000; Yuan et al. 2008; Humphris et al. 2013). The measure has 5 questions, each with 5 reply alternatives from not anxious to extremely anxious. The primary outcome variable was the posttest MDAS total score. The secondary outcome variables were posttest scores for the 2 subscales of the MDAS: anticipatory dental anxiety (MDAS items 1 and 2) and treatment-related dental anxiety (MDAS items 3 to 5). After the intervention and completion of the posttest MDAS, patients reported their gender (female, male, other) and age in full years before attending their scheduled dental appointment. No personal information or information related to dental appointments after the study was collected.

From the MDAS, sums were calculated for the primary outcome total scale (range, 5 to 25) and for the secondary outcomes: anticipatory dental anxiety (range, 2 to 10) and treatment-related dental anxiety (range, 3 to 15).

Sample Size

Power calculation was estimated by the Stata “rsquared” routine. The effect of blocking was not introduced; however, the effect size was set to a low level to ensure a conservative approach when estimating a sufficient sample size. A small effect size of 0.04 in favor of the VRR intervention as compared with TAU would require a sample size of 272 participants at 90% power with alpha set to 0.05, 2-sided. This was calculated by specifying 2 control covariates (pretest MDAS and participant age in years) and the test random assignment factor (0 = TAU, 1 = VRR). Due to the chosen block size of 10 participants, the study required 280 participants.

Randomization

A random allocation sequence was computer generated by A.S. using random number lists in blocks of 10. The blocked randomization was used to keep the numbers of patients in both treatment groups closely balanced during the study and thus to homogenize the variation in group allocation due to patient flow in different weekdays and time of day. The block size of 10 was big enough to prevent guessing the next randomized treatment group, thus reducing bias (Altman 1991). The block size of 10 was also the multiple of number of treatments, and the required sample size was divisible by block size. The students enrolling the participants administered the randomization of patients, allocating the patient to the next free identification number on the randomization list. The patients were blinded until the intervention started. It was not possible to blind the students enrolling the patients.

Statistical Analyses

The primary outcome variable, posttest MDAS total score, was adjusted for the baseline (pretest) MDAS total score and participant age through mixed effects regression with inclusion of the random block effect. The analysis method ignoring blocks is more conservative regarding the statistical significance and thus less efficient and powerless (Matts and Lachin 1988). The analyses were repeated for the secondary outcome variables: MDAS anticipatory and treatment-related dental anxiety. Separate analyses were run for males and females. To avoid making assumptions of strict normality and nonheteroscedasticity, the “robust” option in the “regress” procedure was applied. Residual plots were inspected for identification of possible violations. Alpha was set to 0.05 (2-sided). Data were analyzed with Stata 15.1 (StataCorp 2017).

Ethics

Ethical approval was granted by the City of Helsinki (HEL 2018-008940). The trial was registered at ClinicalTrials.gov (NCT03993080).

Results

The flowchart of allocated and analyzed participants is presented in the Figure. Data collection started October 15, 2018, and was completed February 27, 2019. Recruitment was halted at 277 participants, who were analyzed by original assigned groups. Means and standard deviations for age and the MDAS total, anticipatory, and treatment-related anxiety scores according to gender and intervention group are presented in Table 1. Of the participants, 47.5% reported low dental anxiety (MDAS <10); 43.9%, moderate dental anxiety (MDAS, 10 to 18); and 8.6%, high dental anxiety (MDAS ≥19).

Group had a statistically significant effect in the total MDAS model and anticipatory dental anxiety model (Table 2). The VRR group showed 0.75–MDAS scale unit decrease in total dental anxiety and a 0.43–scale unit decrease in the anticipatory dental anxiety as compared with the TAU group. In the secondary gender-specific analyses, the females in the VRR group showed a >1–MDAS scale unit decrease in dental anxiety as compared with the TAU group. For males, the decrease was not statistically significant. In MDAS anticipatory dental anxiety. the VRR group showed a half–scale unit decrease as compared with the TAU group in females and a 0.2-unit decrease in males. For treatment-related dental anxiety, the decrease in MDAS scores was statistically significant only among females in the VRR group, showing over a half–scale unit decrease as compared with the TAU group (Table 3).

The MDAS outcome data showed a significant level of skewness. The “robust” option in Stata was applied to mitigate this. To check that our analyses were unbiased, we repeated the regression analyses with log-transformed dependent variable. All statistical results remained substantively the same.

Discussion

A short preoperative VRR decreased total and anticipatory dental anxiety in those attending a primary dental care clinic. In the secondary gender-specific analyses, total and treatment-related dental anxiety decreased among females and anticipatory dental anxiety among males. To our knowledge, this is the first study with a short VRR method in a routine dental primary care setting. Like Ganry et al. (2018), we found that even a short application of VRR reduced anticipatory dental anxiety.

It is possible that at least part of the dental anxiety reduction came from distraction, which has been shown to be effective when applied during dental treatment (Frere et al. 2001; Asl Aminabadi et al. 2012; Tanja-Dijkstra et al. 2014; Padrino-Barrios et al. 2015; Atzori et al. 2018; Niharika et al. 2018; Shetty et al. 2019). The virtual reality used in this study was developed for relaxation purposes. Regardless of the pathway, the use of virtual reality preoperatively reduced dental anxiety.

The strengths of this study are the RCT design and the study population, which included participants with all levels of dental anxiety in the primary dental care setting. The levels of dental anxiety were similar to the UK population norms (Humphris et al. 2013). We did not aim to maximize the effect of VRR by recruiting participants with high levels of dental anxiety only. Also, the intervention setup was very similar for both groups in terms of seating and the possibility for the TAU group to use a mobile phone, thus enabling the effect of the virtual reality intervention to be explicitly identified. The study did not assess dental anxiety levels after dental treatment or the type of treatment procedures that participants were receiving. Neither was the content or length of the VRR intervention that participants chose assessed, as this was a population study. Thus, the long-term effects and the effects of different VRR interventions as well as different dental treatments call for further studies.

There are also limitations to the study population. Recruiting took place in a setting with on average 200 patient visits per day. However, most patients arrived just in time for their scheduled appointment and did not have sufficient time to participate in the study (69.5% of those approached and 83.2% of those excluded). This might have led to possible bias in the age distribution, as older patients were more likely to arrive ahead of their scheduled appointments and thus participate the study. As another recruitment bias, we might have missed patients with high dental anxiety, as they may have come at the last minute. However, the percentages of participants with high dental anxiety were similar to the national survey among adult Finns (Liinavuori et al. 2016) and possibly due to the recruitment including patients coming for acute dental care. Only 11.5% of those approached declined to participate for other reasons, and 2.5% did not consent after reading the written information. The fact that many patients were unable to seek out VRR treatment due to time constraints needs to be addressed to ensure successful implementation at the population level.

There was also a lower percentage of men than women in this study, with only 3 men reporting high dental fear in this study. Men with high dental fear were underrepresented in another cohort study where dental anxiety was assessed in conjunction with dental examination (Kankaanpää et al. 2019). This might partly explain the lack of statistical significance of VRR among men and needs to be considered when powering future studies. Thus, results referring to the effect of gender should be interpreted with caution.

The positive findings of this study indicate that a short VRR intervention is a feasible, patient-accepted, inexpensive, and effective way of reducing preoperative dental anxiety in a public dental care setting on a population level. For those who are truly dentally phobic, we realize that more in-depth psychotherapeutic interventions are necessary. We therefore recommend in future studies that the level of dental anxiety be carefully inspected. In addition, further studies are needed to understand the effect of this VRR intervention more fully and to assess long-term outcomes.

Author Contributions

S.Lahti, G. Humphris, contributed to conception, design, data analysis, and interpretation, drafted and critically revised the manuscript; A. Suominen, contributed to design, data analysis, and interpretation, drafted and critically revised the manuscript; R. Freeman, contributed to conception, design, and data interpretation, drafted and critically revised the manuscript; T. Lähteenoja, contributed to design and data interpretation, drafted and critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.

Acknowledgements

The authors acknowledge MelloVR for providing the virtual reality relaxation equipment, the Oral Health Care Unit of Kalasatama Health and Welfare Center of the City of Helsinki for allowing access to their patients, and the students of the Haaga-Helia University of Applied Sciences and Laurea University of Applied Sciences for recruiting the participants.

A supplemental appendix to this article is available online.

The authors received no financial support and declare no potential conflicts of interest with respect to the authorship and/or publication of this article.

ORCID iD

S.Lahti https://orcid.org/0000-0003-3457-4611

Data Accessibility Statement

Data can be requested from the corresponding author.

Among ten runs, the mean and standard deviation of D is quantified and used to evaluate the three guidance approaches along with the time.

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06 Aug
info

Towards AR-assisted visualization and guidance for imaging of dental decay

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons. org/licenses/by/3.0/)

Abstract

Untreated dental decay is the most prevalent dental problem in the world, affecting up to 2.4 billion people and leading to a significant economic and social burden. Early detection can greatly mitigate irreversible effects of dental decay, avoiding the need for expensive restorative treatment that forever disrupts the enamel protective layer of teeth. However, two key challenges exist that make early decay management difficult: unreliable detection and lack of quantitative monitoring during treatment. New optically based imaging through the enamel provides the dentist a safe means to detect, locate, and monitor the healing process. This work explores the use of an augmented reality (AR) headset to improve the workflow of early decay therapy and monitoring. The proposed workflow includes two novel ARenabled features: (i) in situ visualisation of pre-operative optically based dental images and (ii) augmented guidance for repetitive imaging during therapy monitoring. The workflow is designed to minimise distraction, mitigate hand–eye coordination problems, and help guide monitoring of early decay during therapy in both clinical and mobile environments. The results from quantitative evaluations as well as a formative qualitative user study uncover the potentials of the proposed system and indicate that AR can serve as a promising tool in tooth decay management.


1. INTRODUCTION

Oral health problems remain a major public health challenge worldwide in the past 30 years, leading to economic and social burden [1–3]. Wherein, untreated dental decay is the most prevalent issue and is relevant to socio-economic disparities [4, 5]. As shown in Fig. 1, the traditional dental care pattern for dental decay management consists of routine examination in clinics, non-destructive treatments for detected early decays and destructive treatments for irreversible decays. There are three limitations to this pattern. First, visual or tactile examination and the current gold-standard x-ray radiography cannot reliably and timely detect interproximal and occlusal lesions [6], which are the most common types of dental decays. Second, medicine therapy and instructed cleaning are performed by patients at home without supervision. And they need to revisit the dental clinic, which limits the timely monitoring of decay and often leads to further progression of the decay into irreversible decay. Lastly, the treatments for irreversible lesion such as drill-and-fill procedure, root canal treatment and even dental implant are all destructive, painful, expensive and time-consuming. These limitations need to be solved to develop an ideal dental care procedure for decay management, also shown in Fig. 1. If early-stage lesions can be detected reliably, patients can be prescribed with medicinal therapies and instructed/directed cleaning over time outside the dental clinic [3, 7, 8]. Also, if the current clinic-revisiting-based monitoring of decay can be enhanced by monitoring at community health centre or even patient’s home and sharing data with dentists, then timely intervention can be made with fewer clinic-visits and less burden on both dentists and patients [3, 9]. Then, early decays can be detected and healed in time thus avoiding destructive and costly procedures. In need is the continuous research into such ideal management of tooth decay [3].

To move towards this ideal pattern, there have been significant strides towards developing reliable, sensitive and low-cost imaging modalities to diagnose early decays [10, 11]. Three-dimensional (3D) imaging modalities such as cone-beam computed tomography (CBCT) and optical coherence tomography (OCT) are reliable and sensitive but usually require long imaging time on expensive clinical systems. Clinicians typically perform 3D imaging pre-operatively and use the 3D image for planning and intraoperative reference. For intra-operative imaging and also remote monitoring, clinicians also need a 2D imaging modality, e.g. the scanning fibre endoscope (SFE).

Along with the development of imaging modalities, the ease of use for dental imaging needs to be improved in general. Acquiring high-quality images from desired perspective usually requires expert manipulation of the instrument. For example, to effectively monitor the condition of a carious lesion with SFE, users need to image the decay from the same perspective every time, which is difficult without any assistance [12]. Also, using the previous images for navigation requires hand–eye coordination. Clinicians need to divert their attention to the display monitor while manually positioning the scope, additionally compensating for patient’s movement. This is particularly challenging in dental field as there is only manual fixation of patient’s jaw and patients are typically not under local anaesthesia during dental procedures. The above challenges lead to a lengthy learning curve for providing treatment accurately [13, 14]. Moreover, resource-limited areas may lack budgets for well-trained personnel.

In this work, we utilise an augmented reality (AR) head-mounted display (HMD) to develop a platform for visualising dental images from multiple modalities. We also use the HMD as a guidance tool for positioning of an imaging probe during repetitive monitoring of dental lesions and their treatments. We built a prototype system using the Magic Leap One AR headset and two dental imaging modalities OCT and infrared SFE. The key contributions of our work are (i) the design and development of a novel end-to-end system for multi-modal dental image visualisation, (ii) a technique for guided image capture using SFE, and (iii) quantitative evaluations as well as a user study to evaluate the usefulness, usability and limitations of our system and identify areas for future work.

To the best knowledge of the authors, this is the first pilot study to develop HMD-based AR environment for visualisation and guidance for optically monitoring the status of dental lesions. Continued advances in AR devices, dental imaging modalities, as well as systems that combine these two technologies will together push the traditional dental practice towards an ideal future.

2. Related work

Near-infrared (NIR) optical imaging is shown to have the potential to detect early-stage dental decays more reliably [15, 16]. In NIR reflection image, dental decays appear brighter than surrounding sound areas due to increasing scattering coefficient [17]. OCT is a 3D volumetric imaging technique and has been used for NIR imaging of dental decay [18]. Fig. 2a shows a prototype OCT system imaging an extracted human tooth and a slice of the 3D OCT scan where two interproximal dental lesions appear as bright spots. OCT systems are expected to be expensive when introduced to dental clinics, and currently a complete 3D scan takes at least several minutes from prototype systems.

Also, the OCT probe is bulky and requires expert manipulation to acquire high-quality scans. Thus OCT is more suitable as the pre-operative imaging modality used in clinics. The SFE is a 2D imaging technique with the advantages of miniature probe tip and expected low cost. Many SFE prototypes have been used for real-time NIR dental imaging in previous works [19–21]. Fig. 2b shows SFE imaging an extracted human tooth and the SFE image where the white patterns on both sides of tooth indicate two interproximal dental lesions. In the figure, SFE is imaging from the biting surface of tooth, but since NIR light penetrates around 3 mm deep into the surface [20], the interproximal dental lesion under the surface also shows up in the image. This is very helpful for dental decays that are hidden in between the neighboring teeth and not accessible to the operator. Due to the above advantages, SFE is well-suited for quick intraoperative screening and long-term monitoring.

AR technology has been introduced into research areas of dental implant [22–26], oral and maxillofacial surgery [14, 27–29], orthodontics [30] as well as dental education [31, 32]. In previous work, introduction of AR has assisted clinicians by displaying and registering virtual models in the operating field thus reducing difficulty of hand–eye coordination. However, there is as yet no study aimed at assisting dental imaging modalities for detection and monitoring of dental decay [33]. Among all available AR devices, HMDs have the advantage of compactness and intuitiveness (as compared to handheld or armature mounted AR devices). For this study, we chose Magic Leap One [34] AR headset as the hardware platform. Magic Leap One also includes a hand-held controller with a home button, a bumper, a trigger and a touchpad.

3. Methods

The proposed workflow and corresponding technical components are described in Fig. 3. During the initial appointment in dental clinics with high resource availability, a pre-operative 3D raw image is acquired and transferred onto AR headset, and then dentists can examine the 3D image in AR environment intraoperatively and make a diagnosis based on observed position, dimension and severity of dental decays. During this process, the dentist can translate, rotate, and scale the 3D image at will to view it from an optimal viewing angle based on their preference and experience. The dentist can also adjust display parameters including intensity, opacity, and contrast threshold to optimize decay visibility and also account for varying external lighting conditions. Furthermore, they can examine the image by slicing through the 3D structure to accurately locate the decay.

For long-term monitoring, the dentist can select the desired angle of view for future repetitive 2D imaging. Then a virtual model of tooth and imaging instrument, with registered spatial relationships, is generated and stored. During the monitoring phase, 2D imaging can be performed regularly within or outside of a clinical setting, using the virtual model as guidance. In order to reproduce the reference image, the operator aligns the position of the selected tooth and the imaging probe with respect to the virtual model so that the same desired view angle is preserved. Alignment of imaging probe can be done by manual alignment or tracking-based alignment. 2D images are then transferred into AR environment and fused with the 3D image and all previous 2D images for comparison.

The operator or remote dentist can change the desired angle of view according to updated 2D images throughout the period of monitoring. After 2D SFE images are acquired, they are fused with 3D image and transferred to a dentist with computer-aided image analysis for interpretation. By comparing the historical images to the present, the dentist can make determination of whether the dental decay is healing or is progressing under the current prescription and make corresponding adjustment on the prescription (such as frequency and dose of medicine application, and/or time of next dental visit). We prototyped a software system based on this principle using Unity [35] (version 2019.1.0f1) with Magic Leap Lumin SDK [34].

3.1. AR-assisted visualisation of pre-operative 3D image

 In our pilot study, a pre-operative 3D image of the tooth is acquired using a pre-commercial 1310 nm swept-source OCT (Yoshida Dental Mfg., Tokyo, Japan) with 110 nm band and 50kHz scan. The OCT 3D scan is taken from the occlusal view with an imaging range of 10 × 10 × 8 mm3 and an axial imaging resolution of 11 µm. The raw data from OCT imaging system is first converted into point cloud data and down sampled to reduce the data size without losing useful features. The point intensities are then rescaled to increase the dynamic range. The point cloud data is then rendered as a 3D volumetric object using an open-source Unity package for volumetric rendering [36].

Slicing through three orthogonal directions is implemented to allow users to inspect inner structures of the tooth. By examining cross-section slices, dentists can comprehensively inspect the location and size of dental lesions. More importantly, dentists can find out how deep the dental decay has progressed into the dental enamel layer, which would determine whether a drill-and-fill procedure is needed or the medicine treatment should be prescribed with long-term monitoring. Since the visualisation needs to accommodate different lighting conditions and user preferences, adjustment of three display parameters is provided. Users can adjust intensity value to adjust the overall brightness of the volumetric display. They can also adjust the threshold value for saturation, hiding areas that have low contrast. Opacity value can be adjusted to determine the transparency of the volume. Appropriate opacity values allow the user to see the surface structure of tooth as well as inner features like dental decay or a crack without having to inspect through every slice, thus providing an initial and intuitive sense of existence, position and structure of these features. Slicing and display adjustment are implemented as sliders on a panel. The controller is used to select and adjust sliders. The panel and the pre-operative 3D image can be selected by aiming the controller at them and holding down the trigger and physically translating or rotating the controller. When the panel or the image is selected, users can also rescale them by pressing on left of the touchpad to shrink and left of the touchpad to enlarge. See the video in supplementary material for the interaction demo.

3.2. AR-assisted guidance for 2D imaging

Guidance for 2D imaging is necessary not only in that it helps non-dentist personnel to take 2D images at desired view angles, but also in that it guarantees the field of view and perspective of 2D images during repetitive imaging remain the constant and the series of images can be quantitatively compared. After dentists spot decay on the OCT 3D image, they can designate the desired view angle to take 2D images so that the decay can be detected by 2D images. In the view angle selection mode, a virtual cone shape is attached to the end of controller, corresponding to the view frustum of the endoscope. Since NIR SFE has a disc-shaped field of view which grows larger when the target is further away from the probe, a cone can be used to represent the field of view of SFE. The user can aim the cone at the OCT 3D image and adjust the area that is covered by the cone, as shown in Fig. 4a. By pressing the bumper to indicate that the desired view angle is chosen and a virtual reference model consisting of 3D tooth surface model registered with SFE probe model according to indicated view angle is generated for future guidance. The 3D tooth surface model is acquired by an intra-oral scanner (3Shape TRIOS 3, 3Shape, Copenhagen, Denmark).

In this pilot study, we strive to keep the system and workflow as concise as possible, so we are not using any fiducial-point-based tracking which requires an additional tracker. Furthermore, the alignment between the virtual tooth model with the real tooth is done manually by the user. Since the virtual tooth model is the 3D surface structure scan from the same tooth, the user can shrink the model to the same size as the tooth and align them. The next step is to use the reference model for guidance of 2D imaging, where the user needs to align the virtual probe model. The alignment of SFE probe to the virtual model is made more difficult since SFE probe is of a smaller scale. Therefore, we designed two virtual SFE probe models, a cylinder model and a tri-colour-plane model, as shown in Figs. 4b and c.

Besides manual alignment, there are also two tracking-based methods supported by hardware systems on Magic Leap One. The first method is based on image-tracking API provided by Magic Leap [37]. The front-view camera and depth camera on the headset can be used for tracking the spatial position and rotation of a flat image. The target image is printed in the dimension of 3.4 × 3.2 cm2 and attached to the SFE probe. Then the tracked position and rotation of the target image can be transformed to the position and rotation of the probe, assuming the offset between the probe and target image remains rigid and unchanged. The second method is based on the electromagnetic 6-DoF spatial tracking of the control handle [38]. By fixing the SFE probe with the control handle, the tracked position and rotation of the controller can be transformed into the position and rotation of the probe. Once the probe is being tracked, a red cylinder virtual model is shown to indicate the tracked position and rotation. Then the user needs to align the red cylinder virtual model (the tracked position and rotation of the real probe) with the virtual probe model (desired position and rotation for positioning the real probe).

3.3. Data transfer and image fusion

The 2D SFE images are transferred from the instrument to the AR headset via a web server. A polling-based scheme downloads newly acquired images onto the headset, over HTTP. 2D SFE images and the 3D OCT image can then be registered according to the view angles with which the SFE images were taken. As shown in Fig. 5, an occlusal-view SFE image is registered with the OCT 3D image. With the image fusion, users can interpret and compare images from multiple modalities and also inspect the condition of decays during monitoring of therapy.

4. Evaluation

4.1. Experiments

To measure the augmentation quality, we set up a 3D grid coordinate as shown in Fig. 6a. The grid paper has 1 mm fine grids, 5 mm medium grids and 1 cm large grids. Once the hologram is manually aligned with the object, the observer uses a sharp pointer to localise position of a certain point on hologram and then measures the distance between the points on real object and hologram. Jitter and perceived drift of the hologram are quantified by the translation distance measured on the grid paper.

To measure the alignment performance, we also measure the end-to-end accuracy quantified by keypoint displacement in acquired SFE images. We choose to image a USAF resolution test chart as shown in Fig. 6b, to simplify the accurate extraction of keypoints in SFE images. Ten key points are selected on the test chart. The user first aligns the SFE probe in front of the test chart in desired viewpoint and takes one image. Then after putting the SFE probe down for a while, the user realigns the SFE probe with or without guidance and takes another SFE image with the attempt to replicate the same viewpoint as in the first image. Three guidance approaches are used in turn for the guidance of repositioning of SFE probe, among which, ‘without any guidance’ means that user aligns the probe only according to their memory of the desired probe position without referring to real-time SFE video, ‘with AR guidance’ means that user aligns the probe with the AR hint of desired probe position, ‘with video guidance’ means that user aligns the probe by referring to the real-time SFE video and comparing with the reference image. Three guidance approaches are used in random order for ten runs to avoid training bias. The time it takes to realign the probe to desired position is recorded. The x and y positions of the ith keypoint are measured in pixels in reference image and repetitive image as pref xi , pref yi , prep xi , prep yi . The overall keypoint displacement D of the repetitive image is then calculated according to

Among ten runs, the mean and standard deviation of D is quantified and used to evaluate the three guidance approaches along with the time.

4.2. User study

We conducted a user study to get user feedbacks for this prototype. We used a dentoform model with an extracted human tooth installed on it, as shown in Fig. 6c. The extracted human tooth has two artificial dental lesions on its interproximal surfaces. OCT 3D image, occlusal-view SFE 2D image as well as 3D surface shape scan were acquired from this sample, as shown in Fig. 7.

Six subjects were recruited and asked to conduct the tasks with the system, to walk through the workflow. Among the six subjects, three self-reported as dental students or clinicians, while the other three were general users without specialised dental knowledge. All users were new to this AR system and the workflow. The protocol that subjects were asked to perform using the Magic Leap One were as follows: (i) examine the 3D OCT image in the headset by slicing and adjusting display parameters. (ii) Use the cone to select the desired view angle. (iii) Manually align the virtual model with the real tooth. (iv) Align the SFE probe with the virtual probe model and compare two virtual probe models. The manual alignment, image-tracking-based alignment and controller-tracking-based alignment are also compared.

After the tasks were completed, the users were asked to fill out a questionnaire anonymously. See supplementary material for the template of questionnaire.

5. Results and discussion

In the quantitative measurements, we measured the augmentation quality between hologram and objects manually aligned together. We noticed the augmentation quality is influenced by jitter, perceived drift and latency, which degrade perception as well as accuracy and efficiency of the alignment procedure. Jitter is the continuous shaking of the hologram. We measured jitter within the range of 1 mm, which is at the edge of our acceptable range considering the tooth to have a dimension of around 10 mm. Perceived drift is that when the observer moves around a hologram, the perceived position of hologram drifts away. We measured the perceived drift within the range of 5 mm when the observer takes two orthogonal viewpoints. The perceived drift limits users from observing from multiple viewpoints to align probe with the hologram. However, considering that users are not able to freely move around when aligning the probe, the perceived drift may be less fatal for our prototype. Latency is the time lag of hologram update when the user moves their head and is determined by the distance of head movement. The measured latency is within range of 2 s when head motion is within the general range needed for performing the imaging procedure.

We also measured the accuracy of image-tracking-based alignment and controller-tracking-based alignment. The image-tracking-based alignment suffers from limited capability of front-facing camera. The image tracking has an error of up to 4 mm and may lose the target when the printed target image moves fast. Furthermore, when the background of environment is complicated, the image tracking may recognise the wrong target. It is recommended that the image tracking is used in well-lit space while avoiding black or very uniform surfaces as well as reflective surface like mirrors or glasses. The controller-tracking-based alignment suffers from the hologram drift when the electromagnetic sensor is rotated around or moved close to conducting surfaces. All that being said, the current image-tracking and controller-tracking-based alignment approaches suffer from instability and accuracy issues and need improvement either from hardware or from the tracking scheme design. So far, manual alignment seems to be more robust in terms of accuracy and efficiency.

The end-to-end accuracy and efficiency of manual alignment is quantified by the keypoint displacement in acquired reference SFE image and repetitive SFE image with dimension of 400 × 400 pixels. As shown in Table 1, AR guidance has the advantage of better repositioning accuracy compared to without any guidance, and the advantage of faster repositioning speed compared to using SFE real-time video for guidance. By transferring the real-time SFE video to AR headset and placing it near the operating field, we may further improve the accuracy and efficiency of our prototype.

In the user study, the average time taken to educate each subject to use the system to general proficiency (i.e. familiar with the interaction techniques and can use them to accomplish the workflow) was 15 min, which is quite fast considering their unfamiliarity to AR devices. Afterwards, all subjects were able to accomplish the protocol. During the process of prototyping and quantitative evaluation, we thought the following factors may influence the workflow and therefore included qualitative questions regarding their effects. The factors include (i) the latency which may impede the accuracy and efficiency of alignment of the tooth and probe with the virtual models due to the small scale, (ii) the available field of view of the headset. For Magic Leap One, the width and height of the AR field of view are currently the largest in the market and the interface design also avoid borders of frames to mitigate the sense of the limited field of view. However, when the user is too close to the virtual objects, the virtual objects will be cut off by a clipping plane. This limits users to work from a distance of about 37 cm away from the virtual objects, which means that the users may have to always extend their arms away from their body during the alignment tasks. Five subjects felt the latency was noticeable but it did not impede their workflow, while one dental clinician felt the latency of the headset was an impediment. Five subjects reported that the limits of the AR field of view within the headset were unnoticeable, while only one general user thought clipping plane of the headset caused discomfort/distraction during the workflow.

As for feedback on the workflow, three dental personnel all thought the AR-assisted visualisation of OCT is an improvement over standard screen display in the sense of flexible movement in space while preserving the same information as the standard display. Two dental clinicians that are familiar with the OCT image were able to localise the position of both artificial interproximal lesions (decay) and even the natural decay in the groove. The other dental student is not familiar with OCT images so was not able to do this. Although, they commented that the rendering speed of OCT image may be a problem when more 3D scans need to be acquired. All three dental personnel and one general user thought the SFE 2D imaging AR-assisted guidance is easier than without guidance, while two other general users thought it was more difficult. These two general users commented that the manual alignment of the virtual tooth model and the real tooth is complicated due to one major reason. The depth perception does not work well when you want to accurately align virtual object with real object. This is caused by an inherent issue called occlusion leak which has also been reported for other AR devices like Hololens [39] and there’s ongoing research on solving this issue [40]. The image tracking and controller tracking sometimes also suffer from instability. The choice of manual alignment versus tracking-based alignment methods seems to be up to personal preference. In terms of choice of virtual probe model, all three general users prefer the tri-colour-plane model, while three dental personnel have various preference. Therefore it is advantageous to have both virtual probe models available and provide an interface to switch between the two.

This first-ever prototype showed both clinical potential and technical limitations in our study, which we believe will be a useful reference for future research. First, the AR display can relieve clinicians or general users from the troubles of constantly switching views between patient and computer screen and the consequent hand–eye coordination problem. Importantly, the AR display preserves required information in the composite images. Second, this system can assist in the adaptation of multiple dental imaging modalities into clinical use, such as the safe and informative infrared optical imaging. Since images from multiple modalities can be integrated into the system and provide supplementary information for clinicians, this improves the learning curve of clinicians on using these new imaging modalities, and also improves the reliability and sensitivity of dental decay quantification. Notably, the prototype can be easily generalised to other dental imaging modalities available in the clinics, such as CBCT, NIR and fluorescence dental cameras. Also, most of these imaging modalities along with the intra-oral scanners are common in dental clinics. The SFE we use in this study is not commercial but expected to be a low-cost NIR imaging modality. The other addition is the AR headset which continues to get cheaper. Thus, our prototype is both generalisable and cost-effective. Lastly, the proposed solution can help repetitive imaging of dental decay for therapy monitoring, which is the core of the ideal dental care protocol of tooth decay management which maintains the integrity of teeth. There are definite limitations in our prototype reported above. Some limitations stem from the inherent restrictions of the Magic Leap One hardware, such as jitter, perceived drift, latency, occlusion leak and limited FOV. We believe that the rapid progress of AR HMD products will help resolve these limitations. Other limitations stem from our designs on the software and workflow themselves, such as the inaccuracy of manual alignment, which may be resolved by improved designs of tracking mechanism. See supplementary material for the video demo of our system in use.

6. Conclusion

In this work, we proposed an AR-assisted visualisation and guidance system for imaging of dental decay. We introduce a novel workflow which is implemented as a software application on the Magic Leap One AR headset. We evaluated the multimodal system and workflow through quantitative measurements as well as a pilot user study with the recognition that the prototype can be generalisable to other more conventional dental imaging modalities, such as 3D-CBCT and 2D-oral cameras. Thus, with the addition of an AR headset and a low-cost 2D imaging modality like SFE, our prototype can be adapted into dental clinics and rural community health centres.

7. Funding and declaration of interests

Financial support was provided by US NSF PFI:BIC 1631146 award and VerAvanti Inc. Equipment support was provided by NIH/NIDCR R21DE025356 grant and Yoshida Dental Mfg. Corp. A.S. was supported by the University of Washington (UW) Reality Lab, Facebook, Google, and Huawei. Authors have no personal conflicts of interest outside the UW. UW receives license and funding from Magic Leap Inc., and VerAvanti has licensed SFE patents from UW for medical.

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