深度学习膝X线片实现手术类型的自动识别  

Automatic surgical classification of knee X-ray images using machine deep learning

作　　者：马千里 郑明 陈强 郑玉云 郭炯炯[3] 陈育民 赵毅 Ma Qianli;Zheng Ming;Chen Qiang;Zheng Yuyun;Guo Jiongjiong;Chen Yumin;Zhao Yi(Department of Orthopaedics,Fuzhou Second General Hospital,Fuzhou 350007,China;School of Clinical Medicine,Fujian Medical University,Fuzhou 350122,China;Department of Orthopedics,The First Affiliated Hospital of Soochow University,Suzhou 215006,China)

机构地区：[1]福州市第二总医院骨科,福州350007 [2]福建医科大学临床医学部,福州350122 [3]苏州大学附属第一医院骨科,苏州215006

出　　处：《中华创伤骨科杂志》2024年第10期834-841,共8页Chinese Journal of Orthopaedic Trauma

基　　金：福建省自然科学基金(2020J011201);福建省科技创新平台项目(2020Y2014)

摘　　要：目的 将自主开发的深度学习模型对膝X线片中的未手术膝和4种常见手术类型进行自动分类,对模型分类性能进行评估。方法 采集2017年1月至2022年12月在福州市第二总医院骨科接受膝关节手术的患者膝X线片资料。使用百度EasyDL人工智能(AI)开发平台,创建一个多类别物体识别的深度学习模型。采用You only look once算法,将模型在包含1281张膝正位X线片(包括未手术膝、胫骨平台骨折内固定钢板、胫骨高位截骨、全膝关节置换、膝单髁置换)的训练集上训练后生成一个分类器,用于自动识别和分类X线片中的类型。模型分类性能可靠性通过测试集(450张膝正位X线片,类型同上)的准确度、灵敏度、特异度、阳性预测值和阴性预测值5项指标进行评估。绘制受试者操作特征曲线,并计算曲线下面积(AUC),以进一步量化评估模型的分类性能。结果 模型在测试集上的总体准确度为97.0%,敏感度为92.4%,特异度为98.1%,阳性预测值为92.4%,阴性预测值为98.1%,且AUC为0.947,表明该模型在多种类型下的分类能力具有较高的可靠性。具体到各个手术类型的识别,全膝关节置换的5项指标分别为99.1%、99.0%、99.1%、97.1%和99.7%,表明模型识别该类别的性能最好。胫骨平台骨折内固定钢板和胫骨高位截骨的敏感度稍低,分别为87.0%和86.0%。结论 成功构建了一个能够自动识别和分类膝X线片中手术类型的深度学习模型,该模型初步表现出了良好的性能,尤其是在准确度和AUC上。Objective To evaluate the performance of our self-developed deep learning(DL)model which was designed to automatically classify the knee X-ray images into one non-surgical category and 4 surgical categories,including non-surgical knees(NSK),high tibial osteotomy(HTO),total knee arthroplasty(TKA),unicompartmental knee arthroplasty(UKA),and tibial plateau fracture fixation with an internal fixation plate(TPFF).Methods The knee X-ray images were collected of the patients who had undergone knee joint surgery at Department of Orthopaedics,Fuzhou Second General Hospital from January 2017 to December 2022.On the Baidu EasyDL AI platform,a multi-class object recognition DL model was built using the You Only Look Once(YOLO)algorithm.The model was trained on a dataset of 1,281 knee anteroposterior X-ray images(including NSK,HTO,TKA,UKA,and TPFF)to generate a DL model which was able to automatically recognize and classify the knee X-ray images.The reliability of the model classification performance was evaluated by analyzing the 5 indicators[accuracy,sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)]using a test set of 450 knee anteroposterior X-ray images with the above 5 categories.The receiver operating characteristic curve was plotted and the area under the curve(AUC)was calculated to further quantify the classification performance of the model.Results In the test set,on the whole,the model achieved an accuracy of 97.0%,a sensitivity of 92.4%,a specificity of 98.1%,a PPV of 92.4%,and a NPV of 98.1%,an AUC of 0.947,indicating a high reliability in classifying various categories.The model showed a best performance for TKA,with the 5 indicators being 99.1%,99.0%,99.1%,97.1%and 99.7%.The model showed a slightly lower sensitivity for TPFF and HTO(87.0%and 86.0%,respectively).Conclusion A successful DL model has been developed which can automatically classify the knee X-ray images into non-surgical and surgical categories due to its satisfactory performance,particularly in accuracy and

关 键 词：胫骨骨折 骨折固定术 关节成形术 置换 膝 截骨术 深度学习 

分 类 号：R687.3[医药卫生—骨科学]