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 (<.001). A significant difference was observed between the auto- and student self-assessment methods (<.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 (=.085) or posterior (=.14) tooth preparation scores. Notably, the auto-assessment method required significantly shorter time than the expert- and self-assessment methods (<.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 (<.001), whereas the evaluation times for both the tooth types with the auto-assessment method were statistically similar (=.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.

中文翻译：

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用于评估牙体预备体的自动评分和增强现实可视化软件程序

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目视检查，很容易受到检查者的影响。本研究的目的是研究新开发的自动评分和增强现实 (ASAR) 可视化软件程序，用于评估牙齿预备体。牙科学生制作的总共 122 个牙齿模型（61 颗前牙和 61 颗后牙）通过由学生和专家进行的视觉评估以及使用 3 维 ASAR 软件程序进行的自动评估进行了评估(3D)点云比较法。该软件程序提供全面的功能，包括为各个测试模型生成详细报告、为所有测试模型生成同时汇总分数报告、创建 3D 颜色编码偏差图以及形成用于在线的增强现实快速响应 (AR–QR) 代码数据存储与 AR 可视化。通过比较牙体预备评估分数和评估时间来衡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 Benjamini-Hochberg 方法（α=.05）调整成对比较的 Mann-Whitney U 检验。不同评估方法和牙齿类型在预备分数和评估时间方面存在显着差异 (<.001)。在前牙预备体和后牙预备体评分方面，自动评估法和学生自我评估法之间存在显着差异 (<.001)。然而，对于前牙预备分数 (=.085) 或后牙预备分数 (=.14)，自动评估方法和专家评估方法之间没有发现显着差异。值得注意的是，对于两种牙齿类型，自动评估方法所需的时间明显短于专家和自我评估方法 (<.001)。此外，在自我评估方法和专家评估方法中观察到前牙和后牙之间的评估时间存在显着差异 (<.001)，而使用自我评估方法对两种牙齿类型的评估时间在统计上相似 (= .32）。基于 ASAR 的评估与专家评估相当，同时表现出显着更高的时间效率。此外，AR-QR 码通过促进在线数据存储和 AR 可视化来增强学习和培训体验。