Geometry-Aware Abdominal Aortic Aneurysm Digital Twin for Patient-Specific Wall Stress Mapping.

Accurate estimation of wall stress distributions in abdominal aortic aneurysms (AAAs) is critical for improving rupture risk prediction beyond the traditional maximum diameter criterion. While high-fidelity finite element analysis (FEA) provides precise patient-specific wall stress estimates, its computational cost limits its use in real-time clinical decision making. We introduce a graph-based deep learning framework that leverages three types of graph neural networks (GNNs), namely a Gated Graph Convolutional Network (GGCN), an Equivariant Graph Neural Network (EGNN), and a Graph Transformer (GT) to rapidly predict FEA-derived wall stress distributions directly from AAA outer wall surface meshes. Computed tomography angiography (CTA) images from 202 AAA patients treated at three clinical centers, were segmented using an in-house U-Net pipeline and refined manually to generate high quality AAA volume and surface meshes. From these, several node-specific geometric and biomechanical features were recorded, including wall thickness (WallTHK), intraluminal thrombus thickness (ILTTHK), wall strength