Fully Automated Diagnosis of Acute Myocardial Infarction Using Electrocardiograms and Multimodal Deep Learning

BACKGROUND: Rapid detection of acute myocardial infarction (AMI) reduces morbidity and mortality. Deep learning may enhance automated electrocardiogram (ECG) interpretation. OBJECTIVES: The purpose of the study was to develop and validate a deep learning model for AMI detection using ECG data, demographics, and symptoms. METHODS: This retrospective cohort study used ECG data from 2 centers in Västra Götaland County, Sweden (January 2015-June 2023), for model training and validation, with a third center for external testing. Patients with chest pain or dyspnea who received a prehospital or in-hospital ECG were included. A residual convolutional neural network was trained on ECG features, age, sex, and symptoms to predict AMI, defined by International Classification of Diseases codes at discharge. Performance was assessed using area under the receiver operating characteristic, sensitivity, and specificity. RESULTS: The study included 104,507 individuals (208,366 ECGs), with 8.17% in the training set and 8.59% in the external set diagnosed with AMI. The model achieved AUROCs of 0.8221 ± 0.0101 (internal validation ± 95% CI) and 0.8314 ± 0.0085 (external validation). Performance was consistent across sex but slightly lower for ambulance-arriving patients (area under the receiver operating characteristic: 0.8081 ± 0.0095). Saliency maps highlighted focus on ST segments and T waves. CONCLUSIONS: The deep learning model demonstrated strong AMI detection across diverse patient groups. A randomized trial is needed to compare its performance with emergency physicians.