轻量化深度学习模型在口腔诊疗中的应用进展

doi:10.19438/j.cjoms.2025.05.014

中国口腔颌面外科杂志 ›› 2025, Vol. 23 ›› Issue (5): 517-522.doi: 10.19438/j.cjoms.2025.05.014

轻量化深度学习模型在口腔诊疗中的应用进展

高安琪^1,*, 黄昕睿^2,*, 张晓凡³, 张少霆⁴, 洪义³, 陈磊⁵, 王旭东¹

1.上海交通大学医学院附属第九人民医院口腔颅颌面科,上海交通大学口腔医学院,国家口腔医学中心,国家口腔疾病临床医学研究中心,上海市口腔医学重点实验室,上海市口腔医学研究所,上海 200011;
2.上海交通大学信息与电子工程学院,上海 200240;
3.上海交通大学计算科学与工程系,上海 200240;
4.上海商汤善萃医疗科技有限公司,上海 200233;
5.上海科学智能研究院,上海 200232

收稿日期:2025-05-25 修回日期:2025-07-20 发布日期:2025-10-10
通讯作者: 王旭东,E-mail：xudongwang70@hotmail.com
作者简介:高安琪（2001-）,女,博士研究生,E-mail：gaoanqi0220@sjtu.edu.cn;黄昕睿（1998-）,男,博士研究生,E-mail：huangxr@sjtu.edu.cn。^*并列第一作者
基金资助:
国家重点研发计划（2023YFC2414100）; 上海交通大学“交大之星”计划医工交叉研究基金（YG2022ZD014）; 上海市口腔-颅颌面数字化技术研发与应用专业服务平台（21DZ2294600）; 上海市促进产业高质量发展专项（2024-GZL-RGZN-02033）

Advances in the application of lightweight deep learning models in diagnosis and treatment of dental diseases

Gao Anqi¹, Huang Xinrui², Zhang Xiaofan³, Zhang Shaoting⁴, Hong Yi³, Chen Lei⁵, Wang Xudong¹

1. Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology. Shanghai 200011;
2. School of Information and Electronic Engineering, Shanghai Jiao Tong University. Shanghai 200240;
3. Department of Computer Science and Engineering, Shanghai Jiao Tong University. Shanghai 200240;
4. Shanghai Shangtang Shancui Medical Technology Corporation. Shanghai 200233;
5. Shanghai Academy of Artificial Intelligence for Science. Shanghai 200232, China

Received:2025-05-25 Revised:2025-07-20 Published:2025-10-10

摘要/Abstract

摘要： 医学深度学习模型的轻量化通过自适应压缩技术,在保持诊断精度的同时显著降低计算复杂度,使其能够部署于边缘设备,推动AI在临床门急诊、手术引导及便携监测等场景落地。在口腔诊疗领域,轻量化模型的研究采用MobileNet、YOLO等轻量化架构、改进卷积方式、知识蒸馏等技术,完成影像学上牙、颌骨等解剖结构分割,龋齿、口腔癌等疾病诊断,部分集成至智能牙刷、智能手机应用程序等边缘设备。本文综述了轻量化模型在口腔诊疗中的应用进展,为相关技术的进一步发展与临床转化提供参考。

关键词: 轻量化模型, 深度学习, 口腔诊疗, 边缘设备, 实时诊断

Abstract: The lightweighting of medical deep learning models through adaptive compression techniques significantly reduces computational complexity while maintaining diagnostic accuracy, enabling deployment on edge devices and promoting the implementation of AI in clinical scenarios such as emergency care, surgical guidance, and portable monitoring. In the field of dental care, research on lightweight models has employed architectures such as MobileNet and YOLO, along with techniques like convolutional optimization and knowledge distillation, to accomplish imaging-based segmentation of anatomical structures such as teeth and jaws, and the diagnosis of diseases including caries and oral cancer. Some of these models have been integrated into edge devices such as smart toothbrushes and mobile applications. This paper reviewed the application progress of lightweight models in diagnosis and treatment of dental disease, aiming to provide references for further development and clinical transformation of related technologies.

Key words: Lightweight model, Deep learning, Diagnosis and treatment of dental disease, Edge devices, Real-time diagnosis

中图分类号:

R782
R739.8

高安琪, 黄昕睿, 张晓凡, 张少霆, 洪义, 陈磊, 王旭东. 轻量化深度学习模型在口腔诊疗中的应用进展[J]. 中国口腔颌面外科杂志, 2025, 23(5): 517-522.

Gao Anqi, Huang Xinrui, Zhang Xiaofan, Zhang Shaoting, Hong Yi, Chen Lei, Wang Xudong. Advances in the application of lightweight deep learning models in diagnosis and treatment of dental diseases[J]. China J Oral Maxillofac Surg, 2025, 23(5): 517-522.

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轻量化深度学习模型在口腔诊疗中的应用进展

Advances in the application of lightweight deep learning models in diagnosis and treatment of dental diseases

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