Barzegar Gerdroodbary Mostafa, Salavatidezfouli Sajad
Department of Electromechanical Engineering, C-MAST-Center for Mechanical and Aerospace Science and Technology, Universidade da Beira Interior, Covilha, Portugal.
Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
J R Soc Interface. 2025 Mar;22(224):20240774. doi: 10.1098/rsif.2024.0774. Epub 2025 Mar 5.
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.
在本研究中，通过预测性替代模型评估了患有狭窄的患者特异性颈动脉内的血流动力学因素。采用适当正交分解（POD）技术来降低主模型的阶数，进而使用长短期记忆网络来预测主要血流参数，即沿患有狭窄的患者特异性颈动脉的血流速度和压力。所提出的机器学习技术的效率已在有/无狭窄的患者特异性颈动脉中进行了评估。此外，针对不同的POD模态数量进行了重构误差分析。我们的结果表明，心动周期不同阶段的血流速度值对所提出的血液血流动力学估计方法的效率有很大影响。患者特异性颈动脉内狭窄的存在加剧了血流的复杂性，因此，当患者特异性颈动脉存在狭窄时，预测误差的幅度会增加。
