ArticlePLoS computational biology2023
Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A data-driven framework to identify restenosis-prone regions in femoral arteries from geometric and inflow waveform parameters.Journal of the Royal Society, Interface · 2026Article
- Verification of a Fluid-Structure Interaction Model for Aortic Stenosis Through Comparison With In Vitro Experiments.International journal for numerical methods in biomedical engineering · 2026Article
- A Statistical Shape Modeling Approach for the Derivation of a Data-Driven Geometry-Aware Lumped Arterial Stenosis Model.International journal for numerical methods in biomedical engineering · 2026Article
- ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime.PloS one · 2025Article
- The beating heart: artificial intelligence for cardiovascular application in the clinic.Magma (New York, N.Y.) · 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
- Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement.Journal of the American College of Cardiology · 2024Review
- Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning.Frontiers in bioengineering and biotechnology · 2024Article
- Aneurysmal growth in type-B aortic dissection: assessing the impact of patient-specific inlet conditions on key haemodynamic indices.Journal of the Royal Society, Interface · 2023Article
- Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics.Journal of biomechanics · 2023Article
- A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy.
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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 OpenQuestion graph.