Research Professor, Institute for Translational Research in Dentistry, Kyungpook National University, Daegu, Republic of Korea; and Lecturer, Dental School, Hanoi University of Business and Technology, Hanoi, Vietnam.
Professor, UWA Dental School, University of Western Australia, Perth, Australia.
J Prosthet Dent. 2024 Jun;131(6):1104.e1-1104.e8. doi: 10.1016/j.prosdent.2024.02.008. Epub 2024 Mar 14.
Tooth preparation is an essential part of prosthetic dentistry; however, traditional evaluation methods involve subjective visual inspection that is prone to examiner variability.
The purpose of this study was to investigate a newly developed automated scoring and augmented reality (ASAR) visualization software program for evaluating tooth preparations.
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).
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).
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.
牙齿预备是修复牙科的重要组成部分；然而，传统的评估方法涉及主观的视觉检查，容易受到检查者的变化。
本研究旨在探讨一种新开发的自动评分和增强现实（ASAR）可视化软件程序，用于评估牙齿预备。
共评估了 122 个牙模（61 个前牙和 61 个后牙），由牙科学员制作，评估方法包括学生和专家进行的视觉评估，以及使用 3 维（3D）点云比较法的 ASAR 软件程序进行的自动评估。该软件程序提供了全面的功能，包括为每个测试模型生成详细的报告，为所有测试模型生成同时的综合评分报告，创建 3D 彩色偏差图，并形成增强现实快速响应（AR-QR）码，用于在线数据存储和 AR 可视化。通过比较牙齿预备评估分数和评估时间来测量评估方法的可靠性和效率。使用 Kruskal-Wallis 检验对数据进行统计分析，然后使用曼-惠特尼 U 检验对经贝叶斯-霍赫伯格方法（α=.05）调整的两两比较进行分析。
在预备评分和评估时间方面，评估方法和牙齿类型之间存在显著差异（P<.001）。在对前牙和后牙预备进行评分时，自动评估和学生自我评估方法之间存在显著差异（P<.001）。然而，在前牙（P=.085）或后牙（P=.14）预备评分方面，自动评估和专家评估方法之间没有显著差异。值得注意的是，自动评估方法比专家和自我评估方法所需的时间明显更短（P<.001），适用于两种牙齿类型。此外，在自我评估和专家评估方法中，前牙和后牙的评估时间都存在显著差异（P<.001），而自动评估方法对两种牙齿类型的评估时间在统计学上相似（P=.32）。
基于 ASAR 的评估与专家评估相当，同时具有显著更高的时间效率。此外，AR-QR 码通过促进在线数据存储和 AR 可视化，增强了学习和培训体验。
