Mao Kaijing, Thu Khaing Myat, Hung Kuo Feng, Yu Ollie Yiru, Hsung Richard Tai-Chiu, Lam Walter Yu-Hang
Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Computer Science, Hong Kong Chu Hai College, Hong Kong Special Administrative Region, China.
Int Dent J. 2025 Jul 9;75(5):100883. doi: 10.1016/j.identj.2025.100883.
This systematic review aims to evaluate the methodological characteristics and clinical performance of artificial intelligence (AI) models in detecting periodontal disease using digital intraoral photographs. This review includes peer-reviewed publications and conference proceedings in English, focusing on clinical studies of human periodontal diseases. Intraoral photographs served as the primary data source, with fluorescent and microscopic dental images excluded. The methodological characteristics and performance metrics of clinical studies reporting on AI models were analysed. Twenty-six studies met the review criteria. Various image acquisition devices were used by the resarchers including professional cameras, intraoral cameras, smartphones, and home-use devices. Ten studies used clinical examinations as reference methods, while 16 used visual examinations. Eight studies involved multiple experts in dataset annotation. Only 9 studies employed multiple intraoral views for their AI models, with the remaining studies focusing solely on the frontal view. Regarding AI tasks, 17 studies used classification, 4 used detection, and 5 used segmentations. Performance metrics varied widely and were assessed at multiple levels. Classification studies showed accuracies ranging from 0.46 to 1.00, detection studies showed accuracies from 0.56 to 0.78, and segmentation studies achieved Intersection over Union (IoU) scores of 0.43 to 0.70. AI models show potential for detecting periodontal disease from intraoral photographs, but their clinical use faces challenges. Future research should focus on improving reporting standards, standardising evaluation metrics, performing external tests, enhancing data quality, and using clinical gold standards as reference methods. Furthermore, efforts should focus on promoting transparency, integrating ethical considerations, minimising misclassification, and advancing the development of explainable and user-friendly AI systems to enhance their clinical applicability and reliability.
本系统评价旨在评估使用数字化口内照片检测牙周疾病的人工智能（AI）模型的方法学特征和临床性能。本评价纳入了英文的同行评审出版物和会议论文集，重点关注人类牙周疾病的临床研究。口内照片作为主要数据源，排除了荧光和微观牙科图像。分析了报告AI模型的临床研究的方法学特征和性能指标。26项研究符合评价标准。研究人员使用了各种图像采集设备，包括专业相机、口内相机、智能手机和家用设备。10项研究使用临床检查作为参考方法，16项使用视觉检查。8项研究在数据集标注中涉及多名专家。只有9项研究在其AI模型中采用了多个口内视图，其余研究仅关注正面视图。关于AI任务，17项研究使用分类，4项使用检测，5项使用分割。性能指标差异很大，并在多个层面进行了评估。分类研究的准确率范围为0.46至1.00，检测研究的准确率为0.56至0.78，分割研究的交并比（IoU）分数为0.43至0.70。AI模型显示出从口内照片检测牙周疾病的潜力，但其临床应用面临挑战。未来的研究应侧重于提高报告标准、规范评估指标、进行外部测试、提高数据质量以及使用临床金标准作为参考方法。此外，应致力于提高透明度、纳入伦理考量、尽量减少错误分类，并推进可解释且用户友好的AI系统的开发，以提高其临床适用性和可靠性。
