REVIEW article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1635923
This article is part of the Research TopicMedical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine Volume IIView all 18 articles
Efficacy of Artificial Intelligence-Based FFR Technology for Coronary CTA Stenosis Detection in Clinical Management of Coronary Artery Disease: A Systematic Review
Provisionally accepted
Mayidili Nijiati1*
Tong Liu2Ming Liu2Ailiyaerjiang Aisika2Palidanmu Wumaier2Abudukeyoumujiang Abulizi2
Jingru Wang2- 1Radiology, Fourth Affiliated Hospital, Xinjiang Medical University, Urumqi, China
- 2Fourth Affiliated Hospital, Xinjiang Medical University, Urumqi, China
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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.
Keywords: artificial intelligence, Fractional flow reserve (FFR), Coronary computed tomography angiography (CCTA), Coronary Artery Disease, Stenosis detection, deep learning, AI-assisted Quantitative Coronary CTA (AI-QCT) Based Coronary Artery Bypass Grafting
Received: 27 May 2025; Accepted: 21 Jul 2025.
Copyright: © 2025 Nijiati, Liu, Liu, Aisika, Wumaier, Abulizi and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mayidili Nijiati, Radiology, Fourth Affiliated Hospital, Xinjiang Medical University, Urumqi, China
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