Available online 14 March 2024

Author links open overlay panel, , , AbstractStatement of problem

Tooth preparation is an essential part of prosthetic dentistry; however, traditional evaluation methods involve subjective visual inspection that is prone to examiner variability.

Purpose

The purpose of this study was to investigate a newly developed automated scoring and augmented reality (ASAR) visualization software program for evaluating tooth preparations.

Material and methods

A total of 122 tooth models (61 anterior and 61 posterior teeth) prepared by dental students were evaluated by using visual assessments that were conducted by students and an expert, and auto assessment that was performed with an ASAR software program by using a 3-dimensional (3D) point-cloud comparison method. The software program offered comprehensive functions, including generating detailed reports for individual test models, producing a simultaneous summary score report for all tested models, creating 3D color-coded deviation maps, and forming augmented reality quick-response (AR–QR) codes for online data storage with AR visualization. The reliability and efficiency of the evaluation methods were measured by comparing tooth preparation assessment scores and evaluation time. The data underwent statistical analysis using the Kruskal–Wallis test, followed by Mann–Whitney U tests for pairwise comparisons adjusted with the Benjamini–Hochberg method (α=.05).

Results

Significant differences were found across the evaluation methods and tooth types in terms of preparation scores and evaluation time (P<.001). A significant difference was observed between the auto- and student self-assessment methods (P<.001) in scoring both the anterior and posterior tooth preparations. However, no significant difference was found between the auto- and expert-assessment methods for the anterior (P=.085) or posterior (P=.14) tooth preparation scores. Notably, the auto-assessment method required significantly shorter time than the expert- and self-assessment methods (P<.001) for both tooth types. Additionally, significant differences in evaluation time between the anterior and posterior tooth were observed in both self- and expert-assessment methods (P<.001), whereas the evaluation times for both the tooth types with the auto-assessment method were statistically similar (P=.32).

Conclusions

ASAR-based evaluation is comparable with expert-assessment while exhibiting significantly higher time efficiency. Moreover, AR–QR codes enhance learning and training experiences by facilitating online data storage and AR visualization.

Section snippetsMATERIAL AND METHODS

A total of 61 fourth-year dental students prepared 122 artificial maxillary teeth (61 central incisors and 61 first molars) on a dentiform (500A-M; Nissin) fixed inside dental mannequin heads in a preclinical practice course. The students followed the criteria for ceramic crown preparations.9 The study had been approved by the Ethics Committee of Kyungpook National University (KNUDH-2023–08-01).

The prepared tooth models were scanned using a 3D scanner (DOF Freedom HD; DOF). The models were

RESULTS

The Kruskal–Wallis test results showed a significant difference in terms of the preparation score (H=190.4, df=5, P<.001) and evaluation time (H=509.23, df=5, P<.001) across the evaluation methods and tooth types. Descriptive statistics for the tooth preparation score are presented in Table 1. Accordingly, the median score of tooth preparation obtained using the auto-assessment method was 8 for both the anterior and posterior teeth. The student self-assessment median scores were 8 for the

DISCUSSION

This study introduced an innovative method that enabled automatic evaluation of tooth preparations and 3D visualization of the preparation’s deviation in an AR environment. Based on the results, the null hypothesis that there were no differences in tooth preparation assessment and time effectiveness between the evaluation methods, regardless of tooth type, was rejected.

The ASAR reported in this study used an instance-scoring approach based on point-cloud comparison and AR visualization

CONCLUSIONS

Based on the findings of this study, the following conclusions were drawn:

1.

The auto-assessment method proved efficient and consistent for tooth preparation assessment, requiring significantly less time than conventional visual inspection methods.

2.

While being efficient, this method yielded evaluation scores that were statistically comparable with those generated by teacher evaluation.

3.

This software program can serve as a reliable and effective aid, facilitating tooth preparation evaluation for both

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