This project aims to leverage multimodal artificial intelligence (AI) to enhance the prognostic prediction of coronary artery disease (CAD) in the UK Biobank cohort. By integrating diverse data types, including clinical, genetic, imaging, and lifestyle information, we seek to identify novel biomarkers and improve risk stratification for CAD patients. The primary research question is: Can AI-based analysis of multimodal data improve the accuracy of long-term CAD prognosis and uncover previously unrecognized prognostic markers?
The key objectives of this research are to (1) develop an AI-driven model that predicts long-term cardiovascular outcomes, such as myocardial infarction, stroke, and mortality, in CAD patients; (2) identify novel biomarkers and data features, integrating genetic and clinical data with advanced machine learning techniques; and (3) validate these models to ensure their predictive reliability. By improving CAD risk prediction, this research has the potential to refine clinical decision-making, guide personalized treatments, and contribute to more effective public health interventions. Ultimately, the project aims to provide deeper insights into CAD progression and identify actionable biomarkers for early intervention and improved patient outcomes.
