Physics-informed graph neural networks for real-time prediction of wall shear stress in stenotic coronary arteries

Abstract

Abstract Wall shear stress (WSS) is a key hemodynamic parameter associated with atherosclerotic plaque development in coronary arteries. In this study, we developed a physics-informed graph neural network (PI-GNN) for efficient prediction of WSS distributions on stenotic coronary surfaces. Leveraging 40 subject-specific geometries reconstructed from coronary CT angiography, we employed statistical shape modeling to generate a cohort of 1000 synthetic models encompassing systematic variations in stenosis morphology (concentric and eccentric lesions, round and oval cross-sections, single and dual stenoses). Full computational fluid dynamics (CFD) simulations were performed to obtain ground-truth WSS data, which were then mapped onto vessel-surface graphs to train the proposed PI-GNN. The PI-GNN outperformed U-Net (R = 0.85) and multilayer perceptron (R = 0.24) baselines, achieving superior global performance (MAE = 1.05 Pa, RMSE = 5.63 Pa, R = 0.94) while maintaining robust accuracy across all stenosis scenarios. Node-wise Bland–Altman analysis demonstrated negligible mean bias (|bias|< 2 Pa) and narrow 95% limits of agreement, indicating reliable local agreement with CFD, even in complex severe and dual-lesion cases. With inference times reduced to seconds, the proposed PI-GNN serves as a computationally efficient surrogate for real-time clinical decision support and large-scale coronary hemodynamic studies.