BACKGROUND: Delayed or misdiagnosis of acute myocardial infarction (AMI) is not unusual in the daily practice. Since 12- lead electrocardiogram (ECG) is crucial for the detection of AMI, the systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis. AIMS: We aimed to develop a deep learning model (DLM) as a diagnostic support tool based on a 12-lead electrocardiogram. METHODS: 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 not-AMI patients at the emergency department. The DLM was trained and validated by 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. RESULTS: 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). CONCLUSIONS: 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.
BACKGROUND: Delayed or misdiagnosis of acute myocardial infarction (AMI) is not unusual in the daily practice. Since 12- lead electrocardiogram (ECG) is crucial for the detection of AMI, the systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis. AIMS: We aimed to develop a deep learning model (DLM) as a diagnostic support tool based on a 12-lead electrocardiogram. METHODS: This retrospective cohort study included 1,051/697 ECGs from 737/287 coronary angiogram (CAG)-validated STEMI/NSTEMIpatients and 140,336 ECGs from 76,775 not-AMI patients at the emergency department. The DLM was trained and validated by 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. RESULTS: 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). CONCLUSIONS: 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.
Authors: Jie Xu; Yu Zhang; Huamin Yu; Bo Lin; Dejian Wang; Hong Yuan; Bin Hu; Jun Jiang; Peng Xiang; Te Lin; Huizhe Lu; Guiying Zhang Journal: Ann Transl Med Date: 2022-09