口腔疾病防治2026，Vol.34Issue(6)：620-630,11.DOI:10.12016/j.issn.2096-1456.202550456
人工智能在颞下颌关节区影像诊断中的应用研究进展
Advances in the application of artificial intelligence to imaging diagnosis of the temporomandibular joint re-gion
摘要
Abstract
With the rapid development of computer science,the application of artificial intelligence(AI)in the field of medical imaging has become increasingly extensive.The temporomandibular joint(TMJ)is structurally complex,with a high incidence of related disorders and diverse clinical manifestations.This review analyzes the current state of re-search on AI in TMJ imaging diagnosis.Deep learning models based on U-Net and its derivatives have demonstrated outstanding performance in segmentation of condyle and articular disc.Various object detection and feature extraction algorithms have shown excellent diagnostic efficacy for common conditions,such as osteoarthrosis and disc displace-ment,with some models even achieving expert-level performance on test datasets.Meanwhile,explainable AI provides intuitive justification for model decisions through techniques such as heatmap visualization.Notably,current studies still face critical challenges,including coverage of disease spectra,integration of multimodal data,and model generaliz-ability.Future studies should focus on developing integrated systems that combine diagnosis,segmentation,generation,and interpretation functions.Through multicenter data validation and algorithmic optimization,these efforts will en-hance the clinical applicability and decision transparency of models,ultimately laying the foundation for precise imag-ing diagnosis and intelligent management of TMJ disorders.关键词
影像诊断/人工智能/深度学习/图像分割/颞下颌关节紊乱病/退行性骨关节病/关节盘前移位/图像降噪/多模态数据/可解释性人工智能Key words
imaging diagnosis/artificial intelligence/deep learning/image segmentation/temporomandibu-lar disorders/degenerative joint disease/anterior disc displacement/image denoising/multimodal data/ex-plainable artificial intelligence分类
医药卫生引用本文复制引用
陈嘉阳,马若晗,李刚..人工智能在颞下颌关节区影像诊断中的应用研究进展[J].口腔疾病防治,2026,34(6):620-630,11.基金项目
首都卫生发展科研专项(CFH2024-4-4107) （CFH2024-4-4107）
北京市自然科学基金-海淀原始创新联合基金资助项目(L2320029) （L2320029）
北京大学口腔医学院青年科研基金资助(PKUSS20220116) This study was supported by the grants from Capital's Funds for Health Improvement and Research(No.CFH2024-4-4107) （PKUSS20220116）
Beijing Municipal Natural Science Foundation-Haidian Original Innovation Joint Fund(No.L2320029) （No.L2320029）
Youth Research Fund of Peking University School and Hospital of Stomatology(No.PKUSS20220116). （No.PKUSS20220116）
