Systematic review reveals AI-FFR improves diagnostic accuracy of coronary stenosis in CAD, suggesting enhanced clinical management.
Coronary computed tomography angiography (CCTA) integrated with artificial intelligence (AI) technology, particularly AI-based fractional flow reserve (FFR) assessment, has emerged as a crucial tool in the diagnosis and treatment of coronary artery disease (CAD). Recent advances in AI technology have demonstrated promising applications of AI-based FFR in detecting coronary stenosis through CCTA. Current evidence suggests that AI-FFR offers significant advantages in diagnostic accuracy and clinical utility, potentially enhancing the efficiency of CAD management. However, challenges persist in algorithm robustness, data heterogeneity, and clinical implementation. This review synthesizes recent developments in AI-based FFR technology for coronary stenosis detection via CCTA, focusing on AI-assisted quantitative coronary CTA (AI-QCT), deep learning algorithms, and their applications in three-dimensional coronary reconstruction and hemodynamic simulation. We analyze comparative diagnostic performance between AI-FFR and conventional methods, providing insights for future research directions and clinical applications.
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Liu et al. (2025) studied this question.
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