The review summarizes progress toward faster, automated imaging-derived FFR using ML/DL and physics-informed approaches like PINNs and PINOs, while noting challenges in generalizability and the need for clinical validation.
The diagnostic performance of machine learning-based FFRCT for coronary artery disease: A meta-analysis
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.med-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Imaging-Derived Coronary Fractional Flow Reserve: Advances in Physics-Based, Machine Learning, and Physics-Informed Methods
The review summarizes progress toward faster, automated imaging-derived FFR using ML/DL and physics-informed approaches like PINNs and PINOs, while noting challenges in generalizability and the need for clinical validation.