Coronary artery calcium measurement on attenuation correction computed tomography using artificial intelligence: correlation with coronary flow capacity and prognosis

Sang-Geon Cho et al. evaluated the correlation between AI-measured coronary artery calcium (CAC) on attenuation correction CT and myocardial perfusion metrics using [13N]Ammonia PET. AI-CACac negatively correlated with stress myocardial blood flow (ρ = −0.363) and myocardial flow reserve (ρ = −0.305). Patients with AI-CACac > 10 exhibited higher rates of impaired coronary flow capacity (CFC) and significant ischaemia, indicating its relevance for cardiovascular risk stratification. Despite its association with higher rates of major adverse cardiovascular events (MACE), AI-CACac was not an independent predictor after adjusting for clinical risk factors. The AVIEW-CAC software streamlined CAC quantification, enhancing efficiency and consistency. While the study showcases the utility of AI in integrating CAC measurement with myocardial perfusion, its prognostic significance remains inconclusive. Further longitudinal studies are recommended to confirm its predictive value and optimize clinical protocols.