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

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.