深度学习在膝关节骨关节炎磁共振诊断中的研究进展

Research Progress in Deep Learning for Magnetic Resonance Diagnosis of Knee Osteoarthritis

膝关节骨关节炎是一种常见的创伤性、退行性骨关节疾病,膝骨关节各结构的损伤均可诱发不同程度的病变。磁共振图像是膝关节骨关节炎临床诊断的重要依据。目前,运用深度学习模型提取膝关节图像中的深度特征,实现膝关节各结构的分割与病变识别,进而完成膝关节骨关节炎的辅助诊断是现阶段骨关节疾病辅助诊断领域的研究热点。首先讨论膝关节各类成像技术的优缺点,重点概述磁共振多序列成像技术;然后,详述深度学习模型用于膝关节软骨、半月板等组织结构病变诊断的现状;最后,针对现有识别模型存在的问题,对知识蒸馏、联邦学习两种模型的优化技术进行介绍,并对未来的研究方向进行展望。

Knee osteoarthritis is a common traumatic and degenerative bone and joint disease that can induce various pathological changes due to injuries to various knee structures.Magnetic resonance imaging plays a crucial role in the clinical diagnosis of knee osteoarthritis.Currently,the use of deep learning models to extract depth features from knee joint images and achieve segmentation and lesion recognition of various knee joint structures has become a research hotspot in the field of auxiliary diagnosis of knee joint diseases.First,this study discussed the advantages and disadvantages of various imaging techniques for the knee joint,focusing on magnetic resonance multisequence imaging technology.Then,it highlighted current status of deep learning models used for diagnosing knee joint cartilage,meniscus,and other tissue structural lesions.Furthermore,it addressed the limitations of existing recognition models and introduced two model optimization technologies:knowledge distillation and federated learning.Finally,this study concluded by outlining future research directions.