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Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction

arXiv 2410.03802 ↗First posted 4 Oct 2024physics.med-ph cs.LG cs.NAcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 57 references that are in the map. First posted on arXiv 4 Oct 2024.

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21 of 57 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  8. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  9. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  10. 2022GraphCast: Learning skillful medium-range global weather forecastingcited by 261
  11. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  12. 2019What graph neural networks cannot learn: depth vs widthcited by 195
9 more
  1. 2020Expressive Power of Invariant and Equivariant Graph Neural Networkscited by 100
  2. 2020Molecular graph generation with Graph Neural Networkscited by 79
  3. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  4. 2022Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wallcited by 29
  5. 2023Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networkscited by 20
  6. 2022Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphscited by 12
  7. 2023SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasetscited by 11
  8. 2021On the approximation capability of GNNs in node classification/regression taskscited by 9
  9. 2024VC dimension of Graph Neural Networks with Pfaffian activation functionscited by 6

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