Can prediction of acute myocardial infarction using a deep learning-enabled electrocardiogram be used in real-world practice?

Abstract Background Diagnosing acute myocardial infarction (MI) in the emergency department poses challenges. The utilization of a 12-lead electrocardiogram (ECG) is pivotal for detecting ST-segment elevation myocardial infarction (STEMI), yet enhancing ECG interpretation through a systematic algorithm could significantly improve diagnostic accuracy. Nonetheless, delayed diagnosis or misdiagnosis of STEMI is not uncommon in daily practice. Purpose This study aims to develop a fully convolutional network (FCN) based on ECG data as a diagnostic support tool and to evaluate its effectiveness in STEMI detection. Methods A retrospective cohort study involved 3,298 patients with angiographically confirmed STEMI and 37,393 control ECGs, comprising normal readings, false positives, and ECGs mimicking STEMI. The FCN underwent internal training, external validation, and testing across various data splits (80:10:10, 70:15:15, and 60:20:20). Performance evaluation on validation data included assessing the area under the receiver operating characteristic curve (AUC). Subsequently, the trained model predicted unseen data, and sensitivity, specificity, false positives, and false negatives were calculated to evaluate model performance. Results The FCN exhibited exceptional performance in STEMI detection during both internal training and external validation across different data splits. The receiver operating characteristic curves yielded AUC values of 0.995 (80:10:10), 0.994 (70:15:15), and 0.995 (60:20:20). Consistently high values were observed in precision-recall curves, with AUC values of 0.974 (80:10:10), 0.962 (70:15:15), and 0.973 (60:20:20). When utilizing this FCN model in the clinical setting to reanalyze 20 cases of previously misdiagnosed electrocardiograms, it correctly diagnosed 17 cases. Conclusion Integrating the FCN into clinical practice can notably improve the timely and accurate diagnosis of acute MI, reducing instances of delayed or erroneous STEMI diagnoses and facilitating prompt reperfusion therapy.