ArticleComputers in biology and medicine2024
Learning reduced-order models for cardiovascular simulations with graph neural networks.
Article in Computers in biology and medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 53 citations in OpenAlex.
- Graph Neural Networks for Medical Imaging Analysis and Biological Data: Integrating Topology, Geometry, Radiomics, and Generative AI.Bioengineering (Basel, Switzerland) · 2026Review
- Digital twins and digital models of the human circulatory system.Nature reviews bioengineering · 2026Article
- Physics-informed graph neural networks for real-time prediction of wall shear stress in stenotic coronary arteries.Scientific reports · 2026Article
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- Physics constrained graph neural network for real time prediction of intracranial aneurysm hemodynamics.NPJ digital medicine · 2026Article
- Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification.International journal for numerical methods in biomedical engineering · 2026Article
- Full-field surrogate modeling of cardiac electrophysiology encoding geometric variability.Computer methods in applied mechanics and engineering · 2026Article
- Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation.Neuroinformatics · 2025Article
- Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.PLoS computational biology · 2024Article
- Digital twinning of cardiac electrophysiology for congenital heart disease.Journal of the Royal Society, Interface · 2024Article
- Mesh-based GNN surrogates for time-independent PDEs.Scientific reports · 2024Article
- Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.Frontiers in medicine · 2024Article
- Branched Latent Neural Maps.Computer methods in applied mechanics and engineering · 2024Article
- Digital twinning of cardiac electrophysiology for congenital heart disease.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience loss in accuracy when working with anatomies that contain numerous junctions or pathological conditions. We develop one-dimensional reduced-order models that simulate blood flow dynamics using a graph neural network trained on three-dimensional hemodynamic simulation data. Given the initial condition of the system, the network iteratively predicts the pressure and flow rate at the vessel centerline nodes. Our numerical results demonstrate the accuracy and generalizability of our method in physiological geometries comprising a variety of anatomies and boundary conditions. Our findings demonstrate that our approach can achieve errors below 3% for pressure and flow rate, provided there is adequate training data. As a result, our method exhibits superior performance compared to physics-based one-dimensional models while maintaining high efficiency at inference time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.