SOURCE-LINKED INTELLIGENCE

Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Late Gadolinium Enhancement (LGE) on cardiac magnetic resonance is a key marker of myocardial scar, but its limited accessibility motivates routine ECG-based screening. We evaluated whether $β$-variational autoencoder (VAE)-derived ECG representations can discriminate LGE+ from LGE- cardiomyopathic patients in a local cohort of 300 subjects. We compared 32-dimensional features from the foundation ECGx.AI model with those from a shallower $β$-VAE trained on normal PTB-XL ECGs, evaluating downstream classification and Dynamic Time Warping (DTW)-based reconstruction errors. ECGx.AI reached an are

Read original source ↗ Open in workspace

recordType

paper

region

Global