ArticleJournal of the Royal Society, Interface2025
A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis.
Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Engineering design of platelet-mimicking therapeutic systems: multilevel biomimicry, gating strategies, and translational boundaries.Frontiers in bioengineering and biotechnology · 2026Review
- Hemodynamic response to stent-induced aneurysm deformation in patient-specific internal carotid artery cases: A computational study.Scientific reports · 2025Article
- Application of deep reinforcement learning for aerodynamic control around an angled airfoil via synthetic jet.Scientific reports · 2025Article
- Use of proper orthogonal decomposition and machine learning for efficient blood flow prediction in cerebral saccular aneurysms.Scientific reports · 2025Article
- Hemodynamics in carotid artery stenosis.International angiology : a journal of the International Union of Angiology · 2025Review
- Hemodynamic assessment of rupture risk during growth stages in middle cerebral artery aneurysms treated with coiling.Scientific reports · 2025Article
- Impact of aneurysm sac size on the effectiveness of endovascular coiling in patient-specific middle cerebral artery aneurysms: a computational study.Scientific reports · 2025Article
- A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis.Journal of the Royal Society, Interface · 2025Article
- ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime.PloS one · 2025Article
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Authors and funding
2 authors.
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Abstract
In this study, the haemodynamic factors inside the patient-specific carotid artery with stenosis are evaluated via a predictive surrogate model. The technique of proper orthogonal decomposition (POD) is used for reducing the order of the main model and consequently, the long short-term memory is employed for the prediction of main blood flow parameters, i.e. blood velocity and pressure along the patient-specific carotid artery with stenosis. The efficiency of the proposed machine learning technique has been evaluated in patient-specific carotid arteries with/without stenosis. Besides, the reconstruction error analysis is performed for different POD mode numbers. Our results demonstrate that the value of blood velocity at different stages of the cardiac cycle has a great impact on the efficiency of the proposed method for the estimation of blood haemodynamics. The presence of stenosis inside the patient-specific carotid artery intensifies the complexity of the blood flow, and consequently, the magnitude of the errors for the prediction is increased when the stenosis exists in the patient-specific carotid artery.
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