Department of Prosthodontics, Faculty of Dental Sciences, M.S.Ramaiah University of Applied Sciences (RUAS), Bangalore, India.
Department of Oral Pathology & Microbiology, Faculty of Dental Sciences M.S.Ramaiah University of Applied Sciences (RUAS), Bangalore, India.
J Prosthodont. 2024 Jul;33(6):519-532. doi: 10.1111/jopr.13805. Epub 2023 Dec 6.
Uses for artificial intelligence (AI) are being explored in contemporary dentistry, but artificial intelligence in dental shade-matching has not been systematically reviewed and evaluated. The purpose of this systematic review was to evaluate the accuracy of artificial intelligence in predicting dental shades in restorative dentistry.
A systematic electronic search was performed with the databases MEDLINE (PubMed), Scopus, Cochrane Library, and Google Scholar. A manual search was also conducted. All titles and abstracts were subject to the inclusion criteria of observational, interventional studies, and studies published in the English language. Narrative reviews, systematic reviews, case reports, case series, letters to the editor, commentaries, studies that were not AI-based, studies that were not related to dentistry, and studies that were related to other disciplines in dentistry, other than restorative dentistry (prosthodontics and endodontics) were excluded. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (non-randomized experimental studies). A third investigator was consulted to resolve the lack of consensus.
Fifty-three articles were initially found from all the searches combined from articles published from 2008 till March 2023. A total of 15 articles met the inclusion criteria and were included in the systematic review. AI algorithms for shade-matching include fuzzy logic, a genetic algorithm with back-propagation neural network, back-propagation neural networks, convolutional neural networks, artificial neural networks, support vector machine algorithms, K-nearest neighbor with decision tree and random forest, deep learning for detection of dental prostheses based on object-detection applications, You Only Look Once-YOLO. Moment invariant was used for feature extraction. XG (Xtreme Gradient) Boost was used in one study as a gradient-boosting machine learning algorithm. The highest accuracy in the prediction of dental shades was the decision tree regression model for leucite-based dental ceramics of 99.7% followed by the fuzzy decision of 99.62%, and support vector machine using cross-validation of 97%.
Lighting conditions, shade-matching devices and color space models, and the type of AI algorithm influence the accuracy of the prediction of dental shades. Knowledge-based systems and neural networks have shown better accuracy in predicting dental shades.
人工智能（AI）在当代牙科中的应用正在被探索，但 AI 在牙科比色中的应用尚未得到系统的回顾和评估。本系统评价的目的是评估人工智能在修复牙科中预测牙齿比色的准确性。
通过 MEDLINE（PubMed）、Scopus、Cochrane 图书馆和 Google Scholar 进行系统的电子搜索。还进行了手动搜索。所有标题和摘要均符合观察性、干预性研究以及以英语发表的研究的纳入标准。叙述性综述、系统评价、病例报告、病例系列、给编辑的信、评论、非基于人工智能的研究、与牙科无关的研究以及与牙科其他学科（修复学和牙髓学）相关的研究被排除在外。两名调查员通过应用 Joanna Briggs 研究所非随机实验研究批判性评价清单（Quasi-Experimental Studies Critical Appraisal Checklist），独立评估研究的质量评估。如果存在分歧，将咨询第三名调查员以解决。
从所有搜索中总共发现了 53 篇文章，这些文章均发表于 2008 年至 2023 年 3 月期间。共有 15 篇文章符合纳入标准，并被纳入系统评价。用于比色匹配的 AI 算法包括模糊逻辑、具有反向传播神经网络的遗传算法、反向传播神经网络、卷积神经网络、人工神经网络、支持向量机算法、基于决策树和随机森林的 K-最近邻、基于对象检测应用的牙修复体检测的深度学习、YOLO 一次只看一次。矩不变量用于特征提取。在一项研究中，使用 XG（极端梯度）提升作为梯度提升机器学习算法。预测牙色的最高准确率是基于叶蜡石的牙科陶瓷的决策树回归模型，准确率为 99.7%，其次是模糊决策，准确率为 99.62%，交叉验证的支持向量机准确率为 97%。
照明条件、比色设备和颜色空间模型以及 AI 算法的类型会影响牙齿比色预测的准确性。基于知识的系统和神经网络在预测牙齿比色方面显示出更高的准确性。
