Velocity accounts for ~79% of WSS variability in the analytical model while viscosity accounts for ~59% in the patient-specific coronary model, with unary parameter interactions dominating over 93% of total variance in both cases.
Patient-Specific Modeling of Cardiovascular Mechanics,
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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.
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Integrating Uncertainty Quantification into Computational Fluid Dynamics Models of Coronary Arteries Under Steady Flow
Velocity accounts for ~79% of WSS variability in the analytical model while viscosity accounts for ~59% in the patient-specific coronary model, with unary parameter interactions dominating over 93% of total variance in both cases.
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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.