Department
Department of Computer Science
Document Type
Article
Publication Date
4-7-2026
Abstract
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 operators (PINNs and PINOs), as well as key considerations for their clinical translation. Recent Findings ML/DL approaches have markedly improved automation and computational speed, enabling prediction of pressure and FFR from anatomical descriptors or angiographic contrast dynamics. However, their real-world performance and generalizability can remain variable and sensitive to domain shift, due to multi-center heterogeneity, interpretability challenges, and differences in acquisition protocols and image quality. Physics-informed learning introduces conservation structure and boundary-condition consistency into model training, improving generalizability and reducing dependence on dense supervision while maintaining rapid inference. Recent evaluation trends increasingly highlight deployment-oriented metrics, including calibration, uncertainty quantification, and quality-control gatekeeping, as essential for safe clinical use. Summary The field is converging toward imaging-derived FFR methods that are faster, more automated, and more reliable. While ML/DL offers substantial efficiency gains, physics-informed frameworks such as PINNs and PINOs may provide a more robust balance between speed and physical consistency. Prospective multi-center validation and standardized evaluation will be critical to support broad and safe clinical adoption.
Journal Title
arXiv
First Page
1
Last Page
32
Digital Object Identifier (DOI)
https://doi.org/10.48550/arXiv.2602.16000