aceImaging-derived coronary fractional flow reserve: advances in physics-based, machine learning, and physics-informed methods
Purpose of Review Imaging-derived fractional flow reserve (FFR) is rapidly evolving beyond conventional computational fluid dynamics (CFD)-based pipelines toward machine learning (ML), deep learning (DL), and physics-informed approaches that enable fast, wire-free, and scalable functional assessment of coronary artery stenosis. This review synthesizes recent advances in computed tomography (CT)-and angiography-based FFR measurement, with particular emphasis on emerging physics-informed neural networks and neural…
ML, deep learning and physics-informed models can predict coronary pressure and FFR quickly and automatically from imaging, offering wire-free, scalable functional assessment of artery narrowing.
Machine learning FFR models can perform inconsistently across hospitals and are sensitive to domain shift, differing acquisition protocols and image quality, with interpretability challenges.
The source is a narrative review rather than a new trial, and it states that physics-informed methods only 'may' offer better robustness, with prospective multi-center validation still needed.
Evidence
- Peer-reviewedPhysics in Medicine & Biology2026-10-19
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Truvace Impact Record TRV-2026-2080, v1: “Imaging-derived coronary fractional flow reserve: advances in physics-based, machine learning, and physics-informed methods.” Truvace, 2026-10-21. /record/TRV-2026-2080 (accessed at citation time). sha256 542784a4434a7b53…
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