DeepCORO-CLIP: A Multi-View Foundation Model for Comprehensive Coronary Angiography Video-Text Analysis and External Validation

TL;DR

DeepCORO-CLIP is a multi-view foundation model trained on extensive coronary angiography videos, enabling comprehensive, accurate, and rapid analysis of coronary artery disease and related outcomes, with external validation and public release.

Contribution

It introduces a novel multi-view, video-text contrastive learning model for coronary angiography analysis, outperforming existing methods and enabling diverse diagnostic and prognostic tasks.

Findings

Achieved AUROC of 0.89 for stenosis detection externally

Lower mean absolute error in quantitative assessment (13.6%)

Strong performance in predicting adverse cardiovascular events

Abstract

Coronary angiography is the reference standard for evaluating coronary artery disease, yet visual interpretation remains variable between readers. Existing artificial intelligence methods typically analyze single frames or projections and focus mainly on stenosis, limiting comprehensive coronary assessment. We present DeepCORO-CLIP, a multi-view foundation model trained with video-text contrastive learning on 203,808 angiography videos from 28,117 patients across 32,473 studies at the Montreal Heart Institute and externally validated on 4,249 studies from the University of California, San Francisco. DeepCORO-CLIP integrates multiple projections with attention-based pooling for study-level assessment across diagnostic, prognostic, and disease progression tasks. For significant stenosis detection, the model achieved an AUROC of 0.888 internally and 0.89 on external validation. Mean absolute error against core laboratory quantitative coronary angiography was 13.6%, lower than clinical reports at 19.0%. The model also performed strongly for chronic total occlusion, intracoronary thrombus, and coronary calcification detection. Transfer learning enabled prediction of one-year major adverse cardiovascular events with AUROC 0.79 and estimation of left ventricular ejection fraction with mean absolute error 7.3%. Embeddings also captured disease progression across serial examinations. With a mean inference time of 4.2 seconds in hospital deployment, DeepCORO-CLIP provides a foundation for automated coronary angiography interpretation at the point of care. Code, sample data, model weights, and deployment infrastructure are publicly released.