Deep-learning model that estimates a patient's 10-year risk of major adverse cardiovascular events (MACE) directly from a single routine chest radiograph (CXR), intended as an opportunistic risk-assessment tool when the inputs needed for the standard ASCVD risk calculator (lipids, blood pressure, smoking status, etc.) are missing. A 2D convolutional network takes the CXR image alone as input and outputs a continuous 10-year MACE risk estimate. Developed on 147,801 CXRs from 40,718 participants in the PLCO cancer-screening trial and externally validated in 8,869 outpatients with unknown ASCVD risk and 2,132 with known risk at Mass General Brigham, CXR CVD-Risk identified people at elevated MACE risk (adjusted hazard ratio 1.73 for a >=7.5% predicted risk) and provided added discrimination beyond the traditional ASCVD score.

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).

Multimodal cardiac-sensing foundation model pretrained with generative masked pretraining on ECG, PPG, and paired clinical/machine-generated text reports from roughly 1.7 million individuals across three large-scale critical-care and outpatient ECG datasets. A channel-embedding scheme lets the same model accept any combination of 12-lead ECG, single-lead/wearable ECG, and PPG. The resulting embeddings transfer to diagnostic classification, demographic recognition, vital-sign measurement, clinical-outcome prediction, and ECG question answering. Pretrained weights require a signed academic-access agreement rather than an open download.

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.

Largest federated cardiac CT analysis to date (n=8,104 scans) across a real-world federation of German university hospitals, addressing partially-labeled data across sites via a two-step semi-supervised knowledge-distillation strategy: task-specific CNNs first predict on unlabeled data per label type, then a SWIN-UNETR transformer learns from these predictions with label-specific heads. Learns a single federated model that simultaneously predicts TAVI-relevant landmarks (aortic hinge points, coronary ostia, membranous septum) and calcification from cardiac CT, improving generalizability over UNet-based baselines on downstream tasks.

1D residual neural network that predicts a patient's age directly from a 12-lead ECG; the gap between this predicted 'ECG age' and true chronological age is used as a biomarker of cardiovascular risk and mortality. Trained on the CODE-15% Brazilian ECG dataset, with the original R² of 0.71 later reproduced (R² = 0.70) in an independent German validation cohort. Developed by researchers at Uppsala University and UFMG.

Predicts a patient's age from echocardiogram videos across four standard views (PLAX, A2C, A4C, and subcostal), trained on a private multi-site cohort of over 2.6 million videos from more than 166,000 studies across roughly 90,000 patients. The gap between this AI-predicted age and true chronological age is studied as a marker of accelerated or delayed cardiovascular aging and its relationship to all-cause mortality. Uses a 3D CNN (R(2+1)D) with a separate pretrained model per view. Developed by Cedars-Sinai Medical Center and Stanford's Ouyang lab.