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TRUVACE RECORD VERSION
record: TRV-2026-2080
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-21T06:55:01.860920Z
status: published
lens: trace
sector: science
headline: Imaging-derived coronary fractional flow reserve: advances in physics-based, machine learning, and physics-informed methods
dek: 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…
gain_title: 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.
problem_title: Machine learning FFR models can perform inconsistently across hospitals and are sensitive to domain shift, differing acquisition protocols and image quality, with interpretability challenges.
trace_subject: Imaging-derived coronary fractional flow reserve using machine learning and physics-informed methods
gain_reading: 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.
gain_evidence: enable fast, wire-free, and scalable functional assessment of coronary artery stenosis
problem_reading: Machine learning FFR models can perform inconsistently across hospitals and are sensitive to domain shift, differing acquisition protocols and image quality, with interpretability challenges.
problem_evidence: their real-world performance and generalizability can remain variable and sensitive to domain shift | multi-center heterogeneity, interpretability challenges, and differences in acquisition protocols and image quality
quick_read: This review surveys how fractional flow reserve, a measure of how much a coronary narrowing restricts blood flow, is increasingly derived from CT and angiography images. Methods are moving from conventional computational fluid dynamics toward machine learning, deep learning and physics-informed neural networks and neural operators.

The shift matters because it could make functional assessment of coronary stenosis faster and less invasive. Yet the authors say real-world reliability is still unproven: models vary across centers and imaging conditions, and prospective multi-center validation and standardized evaluation are still needed before broad clinical adoption.
limitation: 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.
tag: Dual reading
key_points: Imaging-derived FFR is moving beyond conventional computational fluid dynamics pipelines toward ML, DL and physics-informed approaches. | ML/DL models can predict pressure and FFR from anatomical descriptors or angiographic contrast dynamics. | Physics-informed learning builds in conservation structure and boundary-condition consistency, reducing dependence on dense supervision. | Evaluation increasingly stresses calibration, uncertainty quantification and quality-control gatekeeping for safe use.
rundown: The review covers both CT-based and angiography-based FFR measurement, treating them as routes to wire-free functional assessment. It gives particular emphasis to physics-informed neural networks and neural operators.

Pure data-driven models are described as efficient but potentially fragile, with performance affected by multi-center heterogeneity, interpretability limits and variation in image quality. Physics-informed frameworks are presented as a possibly more robust balance between speed and physical consistency, retaining rapid inference.

The authors point to deployment-oriented evaluation, including calibration, uncertainty quantification and quality-control gatekeeping, as essential. They identify prospective multi-center validation and standardized evaluation as critical next steps.
sources:
- peer_reviewed | Physics in Medicine & Biology | https://doi.org/10.1088/1361-6560/aeb642 | 2026-10-19
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