End-to-end deep learning framework that predicts the procedural outcome of percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) lesions directly from preprocedural coronary CT angiography, aiming to replace slower manual scoring systems (J-CTO, CT-RECTOR, KCCT). The pipeline first segments the coronary artery tree (Patch-UCTNet), detects candidate CTO lesions along the delineated vessel, extracts pathological lesion features with a Swin Transformer, and classifies two outcomes: successful guidewire crossing within 30 minutes and overall PCI success. In the original study, the model completed reconstruction and analysis 85% faster than manual scores (73.7s vs. 418-467s) and was more accurate than the manual CT-RECTOR, KCCT, and J-CTO_CCTA_ scores, reaching an AUROC of 0.97 on the internal test set and 0.96 on an independent external validation cohort (186 patients, 189 CTO lesions).

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.