深度学习在口腔影像分析中的应用  

Application of deep learning in oral imaging analysis

作　　者：杨予萱 谭静怡[1] 周鹂鹂 边子睿 陈伊凡 吴燕岷[1] Yang Yuxuan;Tan Jingyi;Zhou Lili;Bian Zirui;Chen Yifan;Wu Yanmin(Department of Periodontology,the Second Affiliated Hospital of Zhejiang University School of Medicine,Hangzhou 310009,Zhejiang Province,China)

机构地区：[1]浙江大学医学院附属第二医院牙周病专科,浙江省杭州市310009

出　　处：《中国组织工程研究》2025年第11期2385-2393,共9页Chinese Journal of Tissue Engineering Research

基　　金：浙江省基础公益研究计划项目(LY24H140003),项目负责人:谭静怡;浙江省医药卫生科技计划项目(2022498153),项目负责人:谭静怡;浙江省医药卫生科技计划项目(2021427110),项目负责人:周鹂鹂。

摘　　要：背景:近年来深度学习技术越来越多地被运用于口腔医学领域,提高了口腔影像分析的效率及准确率,推动了口腔智能医学的迅速发展。目的:基于口腔影像,阐述深度学习在口腔疾病诊断和治疗方案决策方面的研究现状、优势与局限性,探讨深度学习技术背景下口腔医学变革的新方向。方法:应用计算机检索PubMed数据库中2017年1月至2024年1月发表的深度学习在口腔医学影像领域应用的相关文献,检索词为“deep learning,artificial intelligence,stomatology,oral medical imaging”等,按入组标准筛选后最终纳入80篇文献进行综述。结果与结论:(1)经典的深度学习模型包括人工神经网络、卷积神经网络、递归神经网络和生成对抗网络等,学者们以或竞争或联合的形式运用这些模型,实现更高效的对口腔医学影像的解释。(2)在口腔医学领域,疾病诊断和治疗方案的制定在很大程度上依赖医学影像资料的判读,而深度学习技术拥有强大的图像处理能力,无论是在辅助诊断龋齿、根尖周炎、牙根纵裂、牙周病、颌骨囊肿等疾病方面,还是在辅助第三磨牙拔除术、颈淋巴结清扫术等治疗操作的术前评估方面,深度学习都能帮助临床医生提高决策的准确率与效率。(3)尽管深度学习有望成为口腔疾病诊治的重要辅助工具,但它在模型技术、安全伦理、法律监管方面仍有一定的局限性,未来的研究应侧重于证明深度学习的可推广性、稳健性和临床实用性,寻找将深度学习自动化决策支持系统应用于常规临床工作流程中的最佳方式。BACKGROUND:In recent years,deep learning technologies have been increasingly applied in the field of oral medicine,enhancing the efficiency and accuracy of oral imaging analysis and promoting the rapid development of intelligent oral medicine.OBJECTIVE:To elaborate the current research status,advantages,and limitations of deep learning based on oral imaging in the diagnosis and treatment decision-making of oral diseases,as well as future prospects,exploring new directions for the transformation of oral medicine under the backdrop of deep learning technology.METHODS:PubMed was searched for literature related to deep learning in oral medical imaging published from January 2017 to January 2024 with the search terms“deep learning,artificial intelligence,stomatology,oral medical imaging.”According to the inclusion criteria,80 papers were finally included for review.RESULTS AND CONCLUSION:(1)Classic deep learning models include artificial neural networks,convolutional neural networks,recurrent neural networks,and generative adversarial networks.Scholars have used these models in competitive or cooperative forms to achieve more efficient interpretation of oral medical images.(2)In the field of oral medicine,the diagnosis of diseases and the formulation of treatment plans largely depend on the interpretation of medical imaging data.Deep learning technology,with its strong image processing capabilities,aids in the diagnosis of diseases such as dental caries,periapical periodontitis,vertical root fractures,periodontal disease,and jaw cysts,as well as preoperative assessments for procedures such as third molar extraction and cervical lymph node dissection,helping clinicians improve the accuracy and efficiency of decision-making.(3)Although deep learning is promising as an important auxiliary tool for the diagnosis and treatment of oral diseases,it still has certain limitations in model technology,safety ethics,and legal regulation.Future research should focus on demonstrating the scalability,robustness,and clinical prac

关 键 词：深度学习 口腔医学 口腔影像 疾病诊断 口腔智能医学 

分 类 号：R459.9[医药卫生—治疗学] R318[医药卫生—临床医学] R-1