摘要
目的应用深度学习算法建立基于纸质版12导联体表心电图的人工智能模型,评估并比较其与心律失常专科医生对流出道室性心律失常起源部位定位的效能。方法收集2011年5月1日至2020年12月31日在首都医科大学附属北京朝阳医院、温州医科大学附属第二医院、浙江大学医学院附属邵逸夫医院、北京大学第三医院经射频消融术成功治疗的527例流出道室性心律失常患者的861份纸质版室性心律失常心电图。以术中成功消融靶点作为金标准,对以深度学习为基础的人工智能模型进行训练、验证及测试。两位心律失常专科医生根据经典定位流程图及临床经验对测试集心电图进行靶点判断。对比深度学习算法与人工判读的敏感性、特异性和准确性等。结果基于纸质版常规12导联体表心电图的扫描图像建立的人工智能模型的灵敏度、特异度、准确率、受试者工作特征曲线下面积、F1指数分别为94.0%、83.3%、93.0%、0.93和0.96。针对测试集,专科医生判读的平均灵敏度、特异度和准确率分别为95.7%、83.3%和94.6%。结论在流出道室性心律失常起源定位方面,基于纸质版心电图的深度学习算法表现出较高性能,与心律失常专科医生的判断水平相当。
Objective To develop establish an artificial intelligence model based on paper 12 lead body surface electrocardiogram(ECG)by using deep learning(DL)algorithm,and to evaluate and compare its effectiveness with arrhythmia specialists in locating the origin of outflow tract ventricular arrhythmia.Methods A total of 861 paper ECG were collected from 527 patients who were successfully treated with radiofrequency ablation for OT-VA in Beijing Chao-Yang Hospital,The 2nd Affiliated Hospital of Wenzhou Medical University,Sir Run Run Shaw Hospital of Zhejiang University College of Medicine and Peking University Third Hospital from May 1,2011 to December 31,2020.Taking the successful ablation target as the gold standard,the artificial intelligence model based on DL was trained,verified and tested.Two electrophysiologists judged the target according to the classic localization flow chart and clinical experience and compared its sensitivity,specificity,accuracy with the DL algorithm.Results The DL algorithm achieved an area under the receiver operating characteristic curve(AUC)of 0.93 with sensitivity,specificity,accuracy and F1 scores of 94.0%,83.3%,93.0%and 0.96 respectively,while the electrophysiologists identified the ventricular arrhythmia chamber of origin with a sensitivity of 95.7%,specificity of 83.3%and accuracy of 94.6%.Conclusion In terms of the origin location of outflow tract ventricular arrhythmia,the DL algorithm shows high performance,which was comparable to the judgment level of arrhythmia specialists.
作者
周杨
章德云
魏国栋
薛杨静
陈世权
田颖
洪申达
刘兴鹏
Zhou Yang;Zhang Deyun;Wei Guodong;Xue Yangjing;Chen Shiquan;Tian Ying;Hong Shenda;Liu Xingpeng(Heart Center,Beijing Chao-Yang Hospital,Capital Medical University,Beijing 100020,China;HeartVoice Medical Technology,Hefei 230000,China;Department of Cardiology,The 2nd Affiliated Hospital of Wenzhou Medical University,Wenzhou 325027,China;Department of Cardiology,Sir Run Run Shaw Hospital Affiliated to Medical College of Zhejiang University,Hangzhou 310016,China;National Institute of Health Data Science at Peking University,Beijing 100191,China)
出处
《中华心律失常学杂志》
2022年第2期127-131,共5页
Chinese Journal of Cardiac Arrhythmias
关键词
心电描记术
室性心律失常
深度学习
人工智能
Electrocardiogram
Ventricular arrhythmias
Deep learning
Artificial intelligence
