Artificial Intelligence for Preventing Heart Disease (AiPHD): Observational, Single Center, Prospective and Retrospective Study

Coronary artery disease (CAD) is a leading cause of mortality in western countries. Coronary computed tomography angiography (cCTA) is the first-line imaging test in patients with suspected obstructive CAD. However, in most patients, cCTA shows non-obstructive CAD. The management of patients with non-obstructive CAD is unclear. This is due to the lack of cCTA-based methods capable to assess the risk of disease progression towards developing major adverse cardiovascular events (MACEs) based on the atherosclerosis characteristics of each patient. A solution for prognostication in these patients is particularly appealing since it could allow to identify patients who can benefit of a more aggressive medical treatment and management, thus improving outcome. Proposed methods, which include qualitative evaluations such as the identification of adverse atherosclerotic plaque characteristics or quantitative evaluations such as the quantification of atherosclerotic plaque burden, may in some cases suffer of limited reproducibility between operators and software. Most importantly, each single biomarker is insufficient to accurately predict patient risk, hence potential synergic integration of cCTA and clinical biomarkers is the key to efficiently guide the personalization of patient's management. Furthermore, the few risk stratification methods that have been proposed are not designed to work on platforms capable of deploying the solution to other clinical settings, promoting prospective or external validation