A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis

Mostafa Barzegar Gerdroodbary; Sajad Salavatidezfouli

doi:10.1098/rsif.2024.0774

A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis

Mostafa Barzegar Gerdroodbary^*
, Sajad Salavatidezfouli

^*Corresponding author for this work

Department of Electrical and Computer Engineering - Signal Processing and Machine learning

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

27 Citations (Scopus)

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.

Original language	English
Article number	20240774
Journal	Journal of the Royal Society Interface
Volume	22
Issue	224
ISSN	1742-5689
DOIs	https://doi.org/10.1098/rsif.2024.0774
Publication status	Published - 5 Mar 2025

Keywords

carotid artery
machine learning
non-Newtonian blood flow
stenosis

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title = "A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis",

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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A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis. / Barzegar Gerdroodbary, Mostafa; Salavatidezfouli, Sajad.
In: Journal of the Royal Society Interface, Vol. 22, No. 224, 20240774, 05.03.2025.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis

AU - Barzegar Gerdroodbary, Mostafa

AU - Salavatidezfouli, Sajad

PY - 2025/3/5

Y1 - 2025/3/5

N2 - 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.

AB - 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.

KW - carotid artery

KW - machine learning

KW - non-Newtonian blood flow

KW - stenosis

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JO - Journal of the Royal Society Interface

JF - Journal of the Royal Society Interface

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ER -

A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis

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Keywords

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