Graph Neural Network Graph Embedding Optimization Method for Vascular Stress Prediction

This study focuses on the accurate prediction of vascular stress by using graph neural network, and innovatively proposes a graph embedding method adapted to vascular structure. By comparing with the traditional finite element vertex diagram representation, and deeply analyzing various key factors affecting vascular stress, the efficiency and accuracy of this method are fully verified, which provides potential technical support for vascular mechanics research and related disease diagnosis. In biomedical engineering, vascular stress analysis is very important for the diagnosis and treatment of cardiovascular diseases. Although the traditional finite element analysis is accurate, it has high calculation cost and poor adaptability in the face of complex vascular geometry, material nonlinearity and diverse loads. In this study, an innovative vascular graph embedding method is proposed, and different segments of blood vessels are regarded as vertices to build models, which effectively reduces complexity. GraphSAGE algorithm is used to predict stress, and compared with the traditional finite element vertex graph representation, research is carried out. Comprehensively consider the geometric changes of blood vessels, curvature, uneven pipe diameter and branch structure; Simulating the nonlinearity of materials under physiological and pathological conditions; Set boundary conditions such as vascular end fixation, elastic support and freedom; Covers uniform, concentrated and dynamic blood flow loads. Experiments show that the new method has obvious advantages, the memory occupation of GPU is reduced to about 2%, and the training time is reduced to 4%. Its GraphSAGE model is accurate in stress prediction, and the average accuracy of the maximum von Mises stress is 92.3%. This achievement shows the potential of graph neural network and new graph embedding method in vascular stress analysis, which can provide key support for the research and treatment of vascular diseases and strongly promote the development of biomechanics and medical engineering.