Background: Myocardial perfusion reflects the macro- and microvascular coronary circulation. Recent quantitation developments using cardiovascular magnetic resonance (CMR) perfusion permit automated measurement clinically. We explored the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR, the ratio of stress to rest MBF). Methods: A two center study of patients with both suspected and known coronary artery disease referred clinically for perfusion assessment. Image analysis was performed automatically using a novel artificial intelligence approach deriving global and regional stress and rest MBF and MPR.

Doctors may be able to predict the chances of death, heart attack, and stroke in patients by using cardiac magnetic resonance imaging (MRI) paired with artificial intelligence (AI), potentially making treatment recommendations that will improve patient blood flow and outcomes. The study, conducted by researchers with University College London, was published Friday in the journal Circulation. It was the largest study of its kind to date. Investigators examined and compared the AI-generated blood flow results in patients collected from cardiac MRI scans to assess their risk for an adverse cardiac episode. This analysis showed patients with limited blood flow were more likely to experience negative heart-related outcomes.