基于3D CNN的人工智能辅助诊断模型对超声心动图视频异常改变的识别  

Recognition of abnormal changes in echocardiographic videos by an artificial intelligence assisted diagnosis model based on 3D CNN

作　　者：申凯凯 张喜君[1] 邵人杰 赵明昌 陈建军 袁建军[1] 赵婧革 朱好辉[1] Shen Kaikai;Zhang Xijun;Shao Renjie;Zhao Mingchang;Chen Jianjun;Yuan Jianjun;Zhao Jingge;Zhu Haohui(Department of Ultrasound,Henan Provincial People's Hospital,Zhengzhou 450003,China;CHISON Medical Technologies Co.,LTD.Wuxi 214142,China;Department of Clinical Research Center,Henan ProvincialPeople'sHospital,Zhengzhou450003,China)

机构地区：[1]河南省人民医院超声科,郑州450003 [2]无锡祥生医疗科技股份有限公司,无锡214142 [3]河南省人民医院临床科研服务中心,郑州450003

出　　处：《中华心血管病杂志》2023年第7期750-758,共9页Chinese Journal of Cardiology

基　　金：河南省科技攻关项目(212102310652);江苏省科技成果转化专项资金(BA2018056);2020年度河南省卫健委项目(Wjlx2020380)。

摘　　要：目的:探讨基于三维卷积神经网络(3D CNN)构建的人工智能辅助诊断模型对高血压性心脏病、出现心脏改变的慢性肾功能衰竭(CRF)及甲状腺功能减退症(甲减)患者超声心动图视频的诊断效能及其临床应用价值。方法:本研究为回顾性研究。收集2019年4月至2021年10月就诊于河南省人民医院的高血压性心脏病、出现心脏改变的CRF和甲减患者。依据诊断分为高血压组、CRF组和甲减组,另采用简单随机抽样方法从同期健康体检者中入选正常对照组。收集入选患者的超声心动图视频数据。每组中视频数据按约5∶1的比例分为训练集和独立测试集,采用膨胀3D卷积网络(I3D)对视频进行时空特征的联合提取,对人工智能辅助诊断模型进行训练及测试;训练集和测试集之间无病例交叉。分别使用3种不同切面[单一心尖四腔心(A4C)切面、单一胸骨旁左心室长轴(PLAX)切面、所有切面]视频数据,基于病例或视频建立模型,并进行诊断性能的统计分析,计算敏感度、特异度、受试者工作特征曲线下面积(AUC);并对比人工智能和超声科医师处理病例所需时间。结果:共纳入730例受试者,男性362例(49.6%),年龄(41.9±12.7)岁,共收集了17703条视频。其中,高血压组212例、CRF组210例、甲减组105例、正常对照组203名。使用单一PLAX切面和所有切面数据基于病例进行预测的模型诊断性能较优:(1)高血压组中,使用所有切面数据的模型的敏感度、特异度、AUC分别为97%、89%、0.93,使用单一PLAX切面模型为94%、95%、0.94;(2)CRF组中,使用所有切面数据的模型的敏感度、特异度、AUC分别为97%、95%、0.96,使用单一PLAX切面模型为97%、89%、0.93;(3)甲减组中,使用所有切面数据的模型的敏感度、特异度、AUC分别为64%、100%、0.82,使用单一PLAX切面模型为82%、89%、0.86。3D CNN模型测量和分析每例受试者的超声心动图视频所需时间明显短ObjectiveTo investigate the diagnostic efficiency and clinical application value of an artificial intelligence-assisted diagnosis model based on a three-dimensional convolutional neural network(3D CNN)on echocardiographic videos of patients with hypertensive heart disease,chronic renal failure(CRF)and hypothyroidism with cardiac involvement.MethodsThis study is a retrospective study.The patients with hypertensive heart disease,CRF and hypothyroidism with cardiac involvement,who admitted in Henan Provincial People′s Hospital from April 2019 to October 2021,were enrolled.Patients were divided into hypertension group,CRF group,and hypothyroidism group.Additionally,a simple random sampling method was used to select control healthy individuals,who underwent physical examination at the same period.The echocardiographic video data of enrolled participants were analyzed.The video data in each group was divided into a training set and an independent testing set in a ratio of 5 to 1.The temporal and spatial characteristics of videos were extracted using an inflated 3D convolutional network(I3D).The artificial intelligence assisted diagnosis model was trained and tested.There was no case overlapped between the training and validation sets.A model was established according to cases or videos based on video data from 3 different views(single apical four chamber(A4C)view,single parasternal left ventricular long-axis(PLAX)view and all views).The statistical analysis of diagnostic performance was completed to calculate sensitivity,specificity and area under the ROC curve(AUC).The time required for the artificial intelligence and ultrasound physicians to process cases was compared.ResultsA total of 730 subjects aged(41.9±12.7)years were enrolled,including 362 males(49.6%),and 17703 videos were collected.There were 212 cases in the hypertensive group,210 cases in the CRF group,105 cases in the hypothyroidism group,and 203 cases in the normal control group.The diagnostic performance of the model predicted by cases based on singl

关 键 词：神经网络 人工智能 超声心动描记术 高血压 肾功能衰竭 甲状腺功能减退症 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R540.45[自动化与计算机技术—控制科学与工程]