Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease

Abstract

The 12-lead electrocardiogram (ECG) is inexpensive, noninvasive, and widely available, but conventional interpretation may not capture subtle signals related to cardiac structure, systemic physiology, and future risk. This review examines the use of deep learning–enabled ECG analysis beyond conventional arrhythmia detection and evaluates the maturity, clinical relevance, and implementation challenges of these applications. This narrative review synthesizes landmark studies, external validation cohorts, pragmatic implementation trials, and recent investigations of artificial intelligence–enabled ECG (AI-ECG) models for structural, metabolic, systemic, and prognostic assessment. Particular attention is given to model performance, validation, clinical actionability, and barriers to translation. AI-ECG models have demonstrated the ability to detect reduced left ventricular ejection fraction, hypertrophic cardiomyopathy, valvular disease, cardiac amyloidosis, pulmonary hypertension, hyperkalemia and other dyskalemias, hyperthyroidism, anemia, and sepsis, and to estimate biologic age and mortality risk. Evidence is most mature for screening for reduced left ventricular ejection fraction, supported by large derivation cohorts, external validation, prognostic follow-up, and the EAGLE pragmatic trial. Evidence for many other applications remains retrospective or exploratory, with limitations related to reference standards, generalizability, calibration, disease prevalence, interpretability, and the absence of clearly defined clinical pathways. AI-ECG has the potential to expand the ECG from a conventional diagnostic test into a screening and decision-support platform. Its near-term role is to identify patients who may benefit from confirmatory imaging, laboratory testing, rhythm monitoring, or specialist evaluation. Broader adoption will require prospective validation, workflow integration, subgroup assessment, post-deployment monitoring, regulatory oversight, and evidence that AI-guided care improves outcomes.