Deep learning enhanced magnetocardiography enables multi-task detection of coronary, ventricular, and rhythm disorders

Magnetocardiography (MCG) captures the magnetic fields generated by myocardial currents, theoretically preserving electrophysiological details lost in surface potentials. However, its clinical application has been hindered by small datasets and the complexity of analysing high-dimensional magnetic field data. We sought to develop a self-supervised deep learning framework to detect high burden coronary artery disease (CAD), left ventricular dysfunction, and arrhythmia risk from resting MCG recordings. We developed MCG2Vec, a contrastive deep learning encoder trained on raw 64-channel MCG signals. The model was pre-trained on unlabeled 10-second segments to learn generalizable signal morphology, then fine-tuned for clinical tasks in a retrospective cohort of 1,732 consecutive patients. The primary endpoints were the detection of significant CAD (≥ 70% stenosis), reduced left ventricular ejection fraction (LVEF <55%), and atrial fibrillation (AF) risk, all derived from sinus-rhythm recordings. Model performance was evaluated using patient-stratified five-fold cross-validation and interpreted using Grad-CAM activation mapping. In the validation analysis, the model detected significant CAD with an area under the curve (AUC) of 0.89 (95% CI 0.82 - 0.93) and successfully localized ischemia to the left anterior descending (AUC 0.88), right coronary (0.82), and left circumflex arteries (0.82). Reduced LVEF was identified with an AUC of 0.81 (95% CI 0.71 - 0.91). Furthermore, the model predicted a history of paroxysmal AF from sinus-rhythm recordings with an AUC of 0.77 (95% CI 0.69 - 0.85). Explainability analysis confirmed that the model relied on physiologically distinct phases of the cardiac cycle (repolarization heterogeneity for ischemia and P-wave/S-wave morphology for arrhythmia risk) rather than non-specific noise. Deep learning-enhanced magnetocardiography enables the accurate, non-invasive detection of ischemia, ventricular dysfunction, and arrhythmia risk from a single resting scan. By unlocking the latent diagnostic information within the cardiac magnetic field, this approach offers a scalable, radiation-free adjunct to standard electrocardiography for precision cardiac diagnostics.