Simulation-based machine learning for real-time assessment of side-branch hemodynamics in coronary bifurcation lesions

Abstract

Provisional stenting is the standard treatment for coronary bifurcation lesions, relying on real-time assessment of side-branch (SB) hemodynamics to guide intervention. Functional metrics such as fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are used for this purpose. Still, their application in bifurcation lesions is limited by procedural complexity and the lack of preoperative planning tools. We developed a simulation-based machine learning (ML) framework to predict iFR under resting, and FFR under hyperemic conditions. The framework leveraged a synthetic hemodynamic dataset of 252 bifurcation lesions generated from 7 patient-specific geometries using HARVEY, a massively parallel computational fluid dynamics (CFD) solver. Anatomical variability was incorporated using four linear mixed-effects (LME) models to establish robust predictions. Two clinically relevant data-splitting strategies were evaluated: (1) a scenario excluding untreated cases, simulating models blind to new geometries, and (2) a scenario incorporating matched untreated cases, reflecting real-world conditions where preoperative anatomical data is available. Morphological features, including lesion severity, length, and curvature, were systematically varied alongside inherited anatomical parameters like bifurcation angle and side-branch count. Splitting approach 2 demonstrated superior predictive performance, achieving a maximum diagnostic accuracy of 0.847 (AUC: 0.899) for FFR and 0.797 (AUC: 0.874) for iFR. Mixed-effects models effectively account for patient-specific anatomical variability, with Bland-Altman analyzes confirming minimal bias between CFD and ML predictions. Incorporating preoperative anatomical information reduced variability and improved diagnostic accuracy across the studied thresholds. The proposed ML framework offers precise, real-time functional assessments of SB hemodynamics, reducing procedural uncertainty in provisional stenting strategies using only pre-operative lesion-specific features and a precomputed synthetic hemodynamic dataset of 252 bifurcation lesion instances. Using synthetic data sets and patient-specific anatomical insights, this approach paves the way for personalized coronary intervention planning, bridging the gap between computational modeling and clinical applicability.