Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling.

Patient-specific computational models exhibit strong concordance with invasively measured fractional flow reserve (FFR)-the clinical gold standard for diagnosing coronary ischemia. However, current modeling techniques frequently rely on computationally intensive assumptions such as pulsatile flow dynamics and often fail to optimally leverage patient-specific clinical data that is routinely available, limiting their practical clinical adoption. In this study, we propose a hybrid coronary angiography-based approach that reduces computational complexity through simplified steady-state flow assumptions, while simultaneously better leveraging available clinical information. Specifically, we integrate physics-based modeling with a machine learning (ML) feedback loop designed to refine and improve FFR predictions. We evaluated this hybrid framework using a retrospective two-center cohort comprising 132 patients with 132 coronary lesions. Our results demonstrate that steady-state models effectively capture essential hemodynamic patterns, closely matching pulsatile model predictions. The ML refinement step enhances diagnostic accuracy, yielding a sensitivity of 83.3%, specificity of 100.0%, positive predictive value of 100.0%, negative predictive value of 88.2%, and overall precision of 92.6%. By effectively combining efficient computational modeling with targeted ML-driven refinements, our approach represents a robust, clinically viable solution for accurate patient-specific FFR estimation.