基于深度学习改进的膝骨关节炎自动诊断方法  

Improvements in automatic diagnosis methods for knee osteoarthritis based on deep learning

作　　者：方颖 张延伟 利晞[3] 颜培栋 毕苗 Fang Ying;Zhang Yanwei;Li Xi;Yan Peidong;Bi Miao(The Third Clinical Medical College of Guangzhou University of Chinese Medicine,Guangzhou 510403,Guangdong Province,China;Department of Imaging,the Third Affiliated Hospital,Guangzhou University of Chinese Medicine,Guangzhou 510378,Guangdong Province,China;Department of Radiology,Second Affiliated Hospital,Guangzhou University of Chinese Medicine,Guangzhou 510260,Guangdong Province,China;Zhuhai School of Clinical Medicine,Jinan University,Zhuhai 519009,Guangdong Province,China)

机构地区：[1]广州中医药大学第三临床医学院,广东省广州市510403 [2]广州中医药大学第三附属医院影像科,广东省广州市510378 [3]广州医科大学附属第二医院放射科,广东省广州市510260 [4]暨南大学附属珠海临床医学院,广东省珠海市519099

出　　处：《中国组织工程研究》2025年第35期7511-7518,共8页Chinese Journal of Tissue Engineering Research

摘　　要：背景:膝骨关节炎是一种常见的退行性疾病,不仅严重影响患者的生活质量,同时增加社会医疗负担。早期准确诊断膝骨关节炎对于患者的治疗和预后至关重要,传统的诊断方法不仅主观且耗时,还不能保证稳定的高准确率。目的:开发一种基于深度学习的膝骨关节炎自动诊断方法,利用深度学习网络提高诊断的准确性和效率。方法:在YOLOv8n网络基础上采用Efficient-ViT网络替换YOLOv8n的骨干网络以及增加注意力机制的方法,提出了一种新的网络模型YOLOV8-ViT模型,用于自动识别和分类膝骨关节炎的X射线片图像。实验数据集来自广州中医药大学第三附属医院的5078张膝骨关节炎患者的X射线片图像,由3个影像医师根据Kellgren-Lawrence分级标准采用labelme软件来标注膝关节炎部位并进行分类,采用并集结果。评价指标包括Precision、F1分数、mean average precision(mAP)、Recall、val/box_loss、val/cls_loss和val/dfl_loss。结果与结论:实验结果表明,与YOLOv5n、YOLOv8n、YOLOv9n模型比较,YOLOV8-ViT模型的准确率、IoU阈值为0.5的平均精度(mAP50)、IoU阈值为0.5-0.95的平均精度(mAP50-95)、F1分数和Recall均有所提高,val/box_loss、val/cls_loss和val/dfl_loss分别降低了0.496、0.45和0.523,1.037、0.305和0.728,0.267、0654和0.854,验证了该模型具有较高的检测精度。BACKGROUND:Knee osteoarthritis is a common degenerative disease that significantly impacts patients'quality of life and increases the societal healthcare burden.Early and accurate diagnosis of knee osteoarthritis is crucial for the treatment and prognosis of patients.Traditional diagnostic methods are not only subjective and time-consuming but also do not guarantee consistently high accuracy.OBJECTIVE:To develop an automatic diagnostic method for knee osteoarthritis based on deep learning,utilizing deep learning networks to improve diagnostic accuracy and efficiency.METHODS:A new network model,YOLOV8-ViT,was proposed by replacing the backbone network of YOLOv8n with the Efficient-ViT network and incorporating attention mechanisms for the automatic identification and classification of X-ray images of knee osteoarthritis.The experimental dataset included 5078 X-ray images of patients with knee osteoarthritis obtained from the Third Affiliated Hospital of Guangzhou University of Chinese Medicine.Three imaging physicians annotated the sites of knee osteoarthritis and classified them according to the Kellgren-Lawrence grading standard using Labelme software,and the results were combined.The evaluation indicators used in this study included Precision,F1 score,mean average precision(mAP),Recall,val/box_loss,val/cls_loss,and val/dfl_loss.RESULTS AND CONCLUSION:The experimental results showed that the YOLOV8-ViT model outperformed the YOLOv5n,YOLOv8n,and YOLOv9n models in terms of precision,mAP50,mAP50-95,F1 score,and Recall,while lowering val/box_loss,val/cls_loss,and val/dfl_loss by 0.496,0.45,and 0.523;1.037,0.305,and 0.728;and 0.267,0.654,and 0.854,respectively.These experimental data validate that this model has high detection accuracy.

关 键 词：膝骨关节炎 深度学习 YOLOv8 TRANSFORMER 目标检测 检测精度 

分 类 号：R445[医药卫生—影像医学与核医学] R318[医药卫生—诊断学] TP18[医药卫生—临床医学]