Training and validation of a 12-lead ECG-based deep-learning model for myocardial infarction subtypes

Abstract

Abstract A convolutional neural network was developed to detect acute myocardial infarction (AMI) subtypes from digital 12-lead ECGs. Trained on 173,396 hospitalized patients’ ECGs, the model underwent fine-tuning and internal validation in 7591 patients and external validation in 4370 patients with suspected AMI. Both prospective validation studies employed central diagnostic adjudication. For non-ST-segment elevation MI (NSTEMI), the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.81 [95%-confidence interval (CI) 0.79–0.83], with NSTEMI type 1 at 0.82 [0.80–0.84] versus type 2 at 0.75 [0.71–0.79]. Performance was superior in younger patients without prior heart disease. For ST-segment elevation MI (STEMI), the model outperformed physician interpretation (AUROC 0.96 [95%-CI 0.94–0.98] vs. 0.89 [0.85–0.93], p < 0.001). Occlusion MI detection reached AUROC 0.91 [95%-CI 0.89–0.93], though NSTEMI-OMI cases were frequently missed. Overall calibration was good for all MI subtypes. This ECG-based deep learning model demonstrates good discrimination and calibration across AMI subtypes, indicating potential clinical utility for rapid risk stratification and early cardiology intervention.

The authors' abstract, as published at the source. npj Digital Medicine, 2026 · DOI ↗