AI-Powered Electrocardiography for Detection of Hidden Myocardial Metabolic Stress Before Overt Cardiovascular Disease

Cardiovascular diseases remain the leading cause of mortality worldwide despite significant advances in diagnostic and therapeutic technologies. A substantial body of evidence indicates that myocardial metabolic remodeling and bioenergetic impairment develop long before the onset of overt cardiovascular disease. Conventional electrocardiography, although widely accessible and inexpensive, is traditionally limited to identifying manifest electrical abnormalities and lacks sensitivity for detecting early metabolic stress. Recent advances in artificial intelligence, particularly deep learning models trained on raw electrocardiographic waveforms, have demonstrated the ability to extract latent physiological information embedded within cardiac electrical signals.This study proposes a comprehensive framework for AI-powered electrocardiography aimed at detecting hidden myocardial metabolic stress prior to clinically apparent cardiovascular disease. By integrating multimodal cardiometabolic biomarkers with high-dimensional ECG analysis, this approach seeks to identify early electrophysiological signatures of energetic dysfunction.