Montreal Heart Institute / UCSF / Cedars-Sinai (Harrabi, Avram, Tison, Ouyang et al.)
Multi-view foundation model for coronary angiography trained with video-text contrastive learning on 203,808 angiography videos from 28,117 patients across 32,473 studies at the Montreal Heart Institute, externally validated on 4,249 studies from UCSF. Integrates multiple angiographic projections with attention-based pooling for study-level assessment spanning diagnostic, prognostic, and disease-progression tasks: significant-stenosis detection (AUROC 0.888 internal / 0.89 external), stenosis-percentage estimation (MAE 13.6% vs. 19.0% for clinical reports), chronic total occlusion, intracoronary thrombus, and coronary calcification detection. Transfer learning further enables one-year MACE prediction (AUROC 0.79) and LVEF estimation (MAE 7.3%) from the same angiography embeddings, with a mean in-hospital inference time of 4.2 seconds.
Architecture
Hybrid
Multi-view video-text contrastive (CLIP-style) foundation model: per-projection video encoder with attention-based multi-view pooling, contrastively aligned with a text encoder over angiography report language, for study-level coronary assessment
Framework
PyTorch
Added to catalog
2026-08-10
Montreal Heart Institute Coronary Angiography-Report Archive (DeepCORO-CLIP)
203,808 coronary angiography videos across 32,473 studies from 28,117 patients at the Montreal Heart Institute, paired with angiography report text for video-text contrastive pretraining. A curated public benchmark subset ("DeepCORO-mini", ~1,000 cases / ~7,000 videos) is available via controlled-access request on PhysioNet.
UCSF External Coronary Angiography Validation Cohort (DeepCORO-CLIP)
4,249 external validation studies from the University of California, San Francisco, used to externally validate DeepCORO-CLIP.
Significant coronary stenosis detection from multi-view angiography video
Coronary stenosis percentage estimation from multi-view angiography video (vs. core-lab QCA)
Left ventricular ejection fraction estimation via transfer learning from angiography embeddings
One-year major adverse cardiovascular event (MACE) prediction via transfer learning from angiography embeddings