Practical Barriers to Real-World Implementation of Artificial Intelligence-Driven CT-Derived Fractional Flow Reserve

Abstract

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 [4][5][6].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

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