Novel integration of coronary atherosclerosis imaging with physiologic features for optimized risk stratification of patients with coronary artery disease

This project aims at integrating coronary atherosclerotic and physiologic features for prediction of acute coronary syndromes (ACS).

These features will be studies by developing innovative technological approaches applying machine learning to coronary computed tomography angiography (CCTA) that will provide unique insights into coronary atherosclerosis and pathophysiology, and integrates them into a clinical workflow after validation.

To achieve this level of innovation, we necessarily bring together an exceptional multidisciplinary group of investigators with unparalleled expertise whose unique perspectives will be synergistic for the successful completion of this project.

Our team consists of expertise in coronary artery disease (CAD); CCTA; computational fluid dynamics (CFD); computer vision; machine learning; and clinical trials. Our joint expertise and knowledge is fundamental for this project. The project spans over a period of three years. The first part aims at characterizing coronary pathophysiologic characteristics (CPCs) associated with future ACS.

The second part applies novel machine learning frameworks to integrate CPCs with atherosclerotic anatomic coronary plaque characteristics (APCs) for enhanced identification of stable individuals who will experience future ACS.

The developed machine learning tool will be validated in a unique cohort of stable individuals with suspected CAD, allowing us to determine the primary role of CPCs to patient-centered outcomes.