XplainScar

Explainable machine learning model that detects and localizes left ventricular (LV) scar in hypertrophic cardiomyopathy (HCM) patients directly from 12-lead ECG, as a faster and cheaper alternative to late-gadolinium-enhancement (LGE) cardiac MRI, the clinical gold standard. XplainScar first uses an HCM-specific ECG segmentation algorithm to extract morphological features (duration, amplitude, slope, energy) from the QRS complex, ST segment and T wave of each lead, then combines unsupervised and self-supervised representation learning to predict scar presence and reveal which ECG features are associated with scar location (basal, mid, or apical LV). Trained on 500 HCM patients from the Johns Hopkins HCM Registry and validated on a held-out cohort of 248 HCM patients from UCSF, it reached 88% precision, 90% sensitivity, 78% specificity and an F1-score of 89% for scar detection on the external test set, analyzing a 10-patient batch of ECGs in under one minute.