ReviewJournal of the Royal Society, Interface2021
Data-driven cardiovascular flow modelling: examples and opportunities.
Review in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Development of a Novel Holistic Assessment Strategy of TAVI-Induced Flow Restoration via Dimensionality Reduction Techniques.Cardiovascular engineering and technology · 2026Article
- Quantifying the spatiotemporal mechanical dynamics of engineered cardiac microbundles.PLoS computational biology · 2026Article
- Left atrial flow and thrombosis risk from 4D CT contrast dynamics by physics-informed neural network and indicator dilution theory.bioRxiv : the preprint server for biology · 2026Article
- A comparison of Gaussian processes and polynomial chaos emulators in the context of haemodynamic pulse-wave propagation modelling.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime.PloS one · 2025Article
- Accelerated simulation methodologies for computational vascular flow modelling.Journal of the Royal Society, Interface · 2024Review
- Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics.Journal of biomechanics · 2023Article
- 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
- Feasibility of neural network metamodels for emulation and sensitivity analysis of radionuclide transport models.Scientific reports · 2023Article
- A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries.JVS-vascular science · 2023Review
- Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography.Bioengineering (Basel, Switzerland) · 2022Article
- Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging.IEEE transactions on medical imaging · 2022Article
- Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.Annals of biomedical engineering · 2022Review
- Computational Fluid Dynamics (CFD) For Predicting Pathological Changes In The Aorta: Is It Ready For Clinical Use?Arquivos brasileiros de cardiologia · 2022Article
- Challenges in dynamic mode decomposition.Journal of the Royal Society, Interface · 2021Article
- Data Assimilation by Stochastic Ensemble Kalman Filtering to Enhance Turbulent Cardiovascular Flow Data From Under-Resolved Observations.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.
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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.