Multimodal AI for Precision Preventive Cardiology

Coronary artery disease (CAD) is the leading cause of death worldwide, yet it is highly preventable. Early detection is critical, particularly because the first clinical manifestation of CAD is a heart attack in ∼50% of individuals. Current clinical risk scores rely largely on traditional biomarkers and do not leverage recent advances in medical imaging and genetics. To address this, we developed preCog , a multimodal artificial intelligence framework that integrates multi-organ imaging, genetic risk, metabolic biomarkers, ECGs and demographic data to predict time to incident CAD over up to 10 years of follow-up in a cohort of 60,000 UK Biobank participants. To quantify imaging risk, we applied 2D and 3D foundational computer vision models to more than 500,000 images spanning cardiac, liver and pancreas MRI, as well as DXA scans, each capturing distinct facets of CAD pathophysiology. We extracted deep learning derived image embeddings and compressed them to compact representations that were highly correlated with conventional imaging metrics of cardiac function (e.g. ejection fraction and stroke volumes) yet outperformed these metrics for CAD prediction (AUC 0.794 vs 0.666).

Because imaging cost is a key constraint, we evaluated modality-level contributions and found that only cardiac long-axis cine MRI and aortic distensibility MRI contributed substantial independent value. After adjusting for baseline traits, liver, pancreas and DXA features added no significant predictive power. We constructed a joint time-to-event model integrating imaging, a polygenic risk score (PRS) trained on more than 1.25 million individuals from non-imaged UK Biobank participants, the Million Veteran Program and FinnGen, blood biochemistry and clinical variables. The joint model achieved a C-index of 0.75, exceeding PREVENT (0.71) and Framingham Risk Score (0.66). Importantly we found that imaging and genetic risks were largely independent, indicating that individuals with similar imaging-based risk at a given age may progress differently based on underlying genetic risk. A hierarchical risk stratification framework combining clinical, genetic and imaging data identified a subgroup with a 15-fold increase in incident CAD risk relative to a low-risk baseline. Performance was consistent across data collection centers spanning rural and urban settings and diverse demographics (C-index range 0.73–0.76). Our findings demonstrate the utility of multi-modal AI for medical forecasting of common complex disease to preempt or mitigate their occurrence.