Explainable artificial intelligence identifies and localizes left ventricular scar in hypertrophic cardiomyopathy using 12-Lead electrocardiogram.

ABSTRACT: Left ventricular (LV) scar is a major risk factor for sudden death and heart failure in hypertrophic cardiomyopathy (HCM). LV scar evolves over time and needs longitudinal assessment. Currently, LV scar detection relies on late gadolinium enhancement MRI, which is limited by high cost and artifacts from implanted cardiac devices. To address this, we developed XplainScar, an explainable machine learning model that identifies LV scar using 12-lead electrocardiogram (ECG) data. XplainScar was trained and validated on retrospective data from 748 HCM patients across two centers (500 from Johns Hopkins hospital for model development, and 248 from UCSF for validation). XplainScar employs a combination of unsupervised and self-supervised representation learning to effectively predict scar presence

SUBMITTER: Nezamabadi K 

PROVIDER: S-EPMC12484639 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature