医学
心肌梗塞
接收机工作特性
急诊科
内科学
诊断准确性
心脏病学
心电图
肌钙蛋白
回顾性队列研究
机器学习
曲线下面积
急诊分诊台
队列
曲线下面积
急诊医学
算法
精神科
计算机科学
药代动力学
作者
Wencheng Liu,Chin‐Sheng Lin,Chien‐Sung Tsai,Tien‐Ping Tsao,Cheng-Chung Cheng,Jun‐Ting Liou,Wei‐Shiang Lin,Shu‐Meng Cheng,Yu-Sheng Lou,Chia-Cheng Lee,Chin Lin
出处
期刊:Eurointervention
[European Association of Percutaneous Cardiovascular Interventions]
日期:2021-10-01
卷期号:17 (9): 765-773
被引量:45
标识
DOI:10.4244/eij-d-20-01155
摘要
Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram (ECG) is crucial for the detection of AMI, a systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis.We aimed to develop a deep learning model (DLM) as a diagnostic support tool based on a 12-lead electrocardiogram.This retrospective cohort study included 1,051/697 ECGs from 737/287 coronary angiogram (CAG)-validated STEMI/NSTEMI patients and 140,336 ECGs from 76,775 non-AMI patients at the emergency department. The DLM was trained and validated in 80% and 20% of these ECGs. A human-machine competition was conducted. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of the DLM.The AUC of the DLM for STEMI detection was 0.976 in the human-machine competition, which was significantly better than that of the best physicians. Furthermore, the DLM independently demonstrated sufficient diagnostic capacity for STEMI detection (AUC=0.997; sensitivity, 98.4%; specificity, 96.9%). Regarding NSTEMI detection, the AUC of the combined DLM and conventional cardiac troponin I (cTnI) increased to 0.978, which was better than that of either the DLM (0.877) or cTnI (0.950).The DLM may serve as a timely, objective and precise diagnostic decision support tool to assist emergency medical system-based networks and frontline physicians in detecting AMI and subsequently initiating reperfusion therapy.
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