Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification

This research introduces StenCE, a pretraining framework that enables patient stratification based on features derived from ECGs to support early diagnosis of severe coronary artery stenosis. It aims to overcome the limitations of current diagnostic methods by integrating ECG and angiography representations.