Department of Dental Research Cell, Dr. D. Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India.
Department of Dental Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Clin Exp Dent Res. 2024 Aug;10(4):e70004. doi: 10.1002/cre2.70004.
Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries.
MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method.
A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries.
AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.
龋齿在很大程度上是可以预防的，但仍是一个重要的全球健康问题。许多系统评价总结了人工智能（AI）模型在龋齿诊断和检测中的疗效。因此，本伞式评价旨在综合系统评价中关于 AI 模型在龋齿诊断和检测中的应用和效果的结果。
通过 MEDLINE/PubMed、IEEE Explore、Embase 和 Cochrane 系统评价数据库检索文献。两位作者根据入选标准独立筛选文章，然后评估纳入的文章。研究结果以表格形式总结，并通过叙述方法进行讨论。
共确定了 1249 篇条目，最终纳入了 7 篇。最常使用的 AI 算法是多层感知器、支持向量机（SVM）和神经网络。这些算法被用于从多个来源（包括根尖片、全景片、智能手机图像、咬合片、近红外光透射图像等）进行分割、分类、龋齿检测、诊断和龋齿预测。卷积神经网络（CNN）在龋齿检测、分割和分类测试中表现出高灵敏度、特异性和曲线下面积。值得注意的是，人工智能与根尖片和全景片联合使用，在检测和诊断龋齿方面具有更高的准确性。
AI 模型，特别是基于卷积神经网络（CNN）的模型，在龋齿的准确、客观诊断和检测方面具有巨大的潜力。然而，在成功将其整合到常规实践中时，伦理考虑和谨慎采用仍然至关重要。
