深度学习系统辅助成人发育性髋关节发育不良的分型培训  

Deep-learning system assisted staged training for developmental dysplasia of the hip

作　　者：徐雨凡 胡煜奇 陈泓宇[3] 伊俊瑶 杨立月 王燎[3] 张立箎 王波 XU Yufan;HU Yuqi;CHEN Hongyu;YI Junyao;YANG Liyue;WANG Liao;ZHANG Lichi;WANG Bo(School of Medicine,Kunming University,Kunming 650214,China;School of Biomedical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Department of Orthopaedics,Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200011,China)

机构地区：[1]昆明学院医学院,云南省昆明市650214 [2]上海交通大学生物医学工程学院,上海市200240 [3]上海交通大学医学院附属第九人民医院骨科,上海市200011

出　　处：《组织工程与重建外科》2024年第1期114-120,共7页Journal of Tissue Engineering and Reconstructive Surgery

基　　金：2021年云南省昆明学院省级大学生创新创业训练计划项目(202111393008)。

摘　　要：目的开发一种深度学习系统用于成人发育性髋关节发育不良(Developmental dysplasia of the hip,DDH)患者的Crowe分型辅助诊断,并且分析该系统对于帮助临床医学生掌握DDH分型的可行性。方法纳入149例X线片训练集、42例测试集以及21例验证集,分割盆骨、提取DDH局部图像块,将金标准结果与医学生、AI辅助医学生评估结果进行比较。结果测试集共纳入42例,其中女性30例,男性12例,年龄(69±12)岁,涉及发育不良髋关节67侧(左30侧,右37侧)。AI、医学生、AI辅助医学生评估结果与金标准的相关性为0.906[95%CI(0.850,0.941)]、0.823[95%CI(0.726,0.887)]、0.886[95%CI(0.821,0.929)];准确率分别为0.87、0.78、0.88;精确度分别为0.88、0.83、0.89;召回率分别为0.87、0.78、0.88;F1值分别为0.87、0.80、0.88。混淆矩阵和条件概率结果显示,预测准确率Ⅰ型DDH三组分别为0.98、0.88、0.96,Ⅱ型DDH三组分别为0.40、0.20、0.40,Ⅲ型DDH三组分别为0.56、0.67、0.78;Ⅳ型DDH三组分别为0.88、0.75、0.88。结论深度学习辅助诊断系统可以有效提高医学生对于DDH分型的评估能力,可作为医学生学习掌握DDH影像诊断的培训工具。Objective To develop a deep learning system for assisted diagnosis of Crowe staging in adult patients with developmental dysplasia of the hip(DDH),and to analyze the feasibility of the system in assisting clinical medical students to master DDH staging.Methods A training set of 149 X-rays,a test set of 42 cases,and a validation set of 21 cases were included,and the pelvis was segmented,localized image blocks of DDH were extracted,and the gold-standard results were compared with those assessed by medical students and AI-assisted medical students.Results A total of 42 cases,including 30 females and 12 males,aged(69±12)years,were included in the test set,and 67 dysplastic hips were involved(30 on the left and 37 on the right).The correlation of the AI,medical student,and AI-assisted medical student assessment results with the gold standard was 0.906[95%CI(0.850,0.941)],0.823[95%CI(0.726,0.887)],0.886[95%CI(0.821,0.929)].The accuracy of AI,medical students and AI-assisted medical students was 0.87,0.78 and 0.88,the precision was 0.88,0.83 and 0.89,the recall rate was 0.87,0.78 and 0.88,and F1 value was 0.87,0.80 and 0.88,respectively.The results of the confusion matrices and conditional probabilities showed that the accuracy of the three groups of typeⅠwere 0.98,0.88,0.96,and 0.40,0.20,0.40 for typeⅡtrio,and 0.56,0.67,0.78 for typeⅢtrio,and 0.88,0.75,0.88 for typeⅣtrio.Conclusion Deep learning-assisted diagnostic system can effectively improve the medical students'assessment of the DDH patients with various types of DDH,and can be used as a training tool for medical students to learn and master the diagnosis of DDH imaging.

关 键 词：发育性髋关节发育不良 人工智能 深度学习 影像 医学生教学 

分 类 号：R684.2[医药卫生—骨科学]