Physics-informed graph neural networks for real-time prediction of wall shear stress in stenotic coronary arteries
Year: 2026
Type: article
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.
Source: Scientific Reports
Authors: Ting-Ting Luo, Li Yang, Jie Chen, Zi-Wen Wu, Jie Chang, Yan-Tao Zhang
Institutions: Wannan Medical College, Anhui Normal University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G3163117357
OpenAlex ID: W7150848551