A Deep-learning Algorithm With the Real World Validation for Detecting Acute Myocardial Infarction

Abstract BackgroundThe initial detection and diagnosis of ST-segment or non-ST-segment elevation myocardial infarction (STEMI or NSTEMI) definitely rely on a 12-lead electrocardiogram (ECG). Delay or misdiagnosis is not unusual by subjective interpretation. Our aim is to develop a DLM as a diagnostic support tool to detect MI based on a 12-lead ECG and to evaluate the performance of this model.MethodsThis study included 1,051 ECGs from 737 coronary angiography (CAG)-validated STEMI patients, 697 ECGs from 287 CAG-validated NSTEMI patients, and 140,336 not-MI ECGs from 76,775 patients at emergency departments. DLM was trained and validated for the performance using 80% and 20% of the ECGs, respectively. 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 DLM and experts. STEMI versus not-STEMI, and MI versus not-MI were evaluated by DLM.ResultsThe AUCs of DLM for identifying STEMI and MI were 0.976 and 0.944 in the human-machine competition, respectively, which were significantly better than those of our best clinicians. In the real world setting, DLM presented with AUC of 0.995/0.916 with corresponding sensitivities of 96.9%/77.0%, and specificities of 96.2%/92.9% in the identification of STEMI and MI, respectively. Furthermore, DLM demonstrated sufficient diagnostic capacity for STEMI without the aid of troponin I (TnI) (AUC= 0.996) with corresponding sensitivity and specificity of 98.4% and 96.9%. The AUC of combined DLM and the first recorded TnI for the detection of NSTEMI were increased to 0.978 with corresponding sensitivity and specificity of 91.6% and 96.7%, which was better than that of DLM (0.877) or TnI (0.949) alone. ConclusionsDLM may serve as a diagnostic decision tool to assist intensive or emergency medical system-based networks and frontline physicians in identifying STEMI and NSTEMI in a timely and precise manner to prevent delay or misdiagnosis, and thereby to facilitate subsequent reperfusion therapy.