A multicentre research team has for the first time harnessed the power of Artificial Intelligence (AI) to instantly and accurately measure blood flow to the heart, helping predict which patients may suffer myocardial infarction (MI) or stroke.
Professor James Moon, who led the study, sees the advance as significant and is optimistic that AI analysis of perfusion mapping will be a reliable, convenient, and detailed new biomarker in cardiac patient care. With myocardial perfusion reflecting the macro- and microvascular coronary circulation, researchers from University College London (UCL) and Barts Health NHS Trust collaborated with colleagues at the National Institutes for Health in the USA to explore the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR − the ratio of stress to rest MBF) in patients with suspected and known coronary artery disease referred clinically for perfusion assessment.
The team conducted image analysis automatically, using a novel AI approach, to seek the associations of stress MBF and MPR with death and major adverse cardiovascular events, including MI, stroke, heart failure, late (>90 days) revascularization, and death.
The study concluded: ‘In patients with known or suspected coronary artery disease, reduced MBF and MPR measured automatically inline using AI quantification of cardiovascular magnetic resonance (CMR) perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcomes’.