用图神经网络预测主动脉瘤破裂风险，速度提升且精度不降。
Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction
- 基于有限体积网格构建图结构，用GNN学习血流应力分布。
- 在不同生长阶段预测壁面剪切力与振荡剪切指数，准确率高。
- 适合临床快速评估动脉瘤风险，尤其适用于个性化诊疗。
心血管系统的复杂性需被精准还原以及时识别健康问题；为此，先进的多保真度、多物理场数值模型至关重要。全阶模型（FOM）虽能提供精确的血流动力学评估，但计算成本过高，难以实现实时临床应用。相反，降阶模型（ROM）可在保证精度的同时显著提升效率，对个性化医疗和及时临床决策尤为关键。本文通过将全阶模型与降阶模型结合，利用计算流体动力学（CFD）预测胸主动脉瘤的生长与破裂风险。采用图神经网络（GNN）对不同生长阶段的壁面剪切应力（WSS）和振荡剪切指数（OSI）进行预测。GNN利用有限体积法（FV）离散所得网格的天然图结构，充分捕捉空间局部信息，且不受输入图维度影响。实验验证框架结果令人鼓舞，证实该方法可有效克服维度灾难问题。
原文摘要 · Abstract (English)
The complexity of the cardiovascular system needs to be accurately reproduced in order to promptly acknowledge health conditions; to this aim, advanced multifidelity and multiphysics numerical models are crucial. On one side, Full Order Models (FOMs) deliver accurate hemodynamic assessments, but their high computational demands hinder their real-time clinical application. In contrast, Reduced Order Models (ROMs) provide more efficient yet accurate solutions, essential for personalized healthcare and timely clinical decision-making. In this work, we explore the application of computational fluid dynamics (CFD) in cardiovascular medicine by integrating FOMs with ROMs for predicting the risk of aortic aneurysm growth and rupture. Wall Shear Stress (WSS) and the Oscillatory Shear Index (OSI), sampled at different growth stages of the thoracic aortic aneurysm, are predicted by means of Graph Neural Networks (GNNs). GNNs exploit the natural graph structure of the mesh obtained by the Finite Volume (FV) discretization, taking into account the spatial local information, regardless of the dimension of the input graph. Our experimental validation framework yields promising results, confirming our method as a valid alternative that overcomes the curse of dimensionality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。