Imaging-derived coronary fractional flow reserve using machine learning and physics-informed methods

Source article: Imaging-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…

By the Truvace deskOctober 21, 20262 min read1 sourcePeer-reviewedTRV-2026-2080· cite & verify

Imaging-derived coronary fractional flow reserve: advances in physics-based, machine learning, and physics-informed methods — Coronary catheterization1 by www.servier.com. CC BY 3.0 · https://creativecommons.org/licenses/by/3.0

Trace impact readingContested

P 69

Both sides are scored from claims and sources, not community votes.

G 67

In brief

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.

Main 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.

The gain

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.

The problem

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 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.

What this doesn’t fix

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.