Noninvasive coronary CT angiography (CCTA) has been firmly established as a first-line imaging modality for evaluating suspected coronary artery disease (CAD) 1,2.Nevertheless, the well-known discordance between the severity of anatomical stenosis and functional ischemia remains a central challenge, particularly for intermediate lesions 3.CT-derived fractional flow reserve (CT-FFR) extends cardiac CT from morphology to physiology and has consistently demonstrated improved diagnostic accuracy compared with CCTA alone 456.Computational fluid dynamics-based off-site CT-FFR (e.g., HeartFlow) has been the most extensively validated, and artificial intelligence (AI)-based on-site CT-FFR aims to enhance workflow integration and scalability 7.AI-based CT-FFR is widely anticipated as a scalable and workflow-integrated solution that offers rapid on-site analysis without additional scanning 5,8.In randomized
Kim et al. (Thu,) studied this question.