Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine

Abstract

IntroductionThe computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS).MethodsA total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods.ResultsANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice.

CFD Based on The Visualisation of Aortic Valve Mechanism in Aortic Valve Stenosis for Risk Prediction at The Peak Velocity

“…Wall shear stress and time-averaged wall shear stress (TAWSS) are essential parameters to consider in the context of aortic valve stenosis [16]. It refers to the tangential force per unit surface area exerted by flowing blood on the vessel wall [17].…”

Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks

“…The primary objective of employing ML in CFD is to optimize various aspects, including the acceleration of simulations, as seen in direct numerical simulations ( Bar-Sinai et al, 2019 ), the improvement of turbulence models, and the development of reduced-order models ( Duraisamy et al, 2019 ; Vinuesa and Brunton, 2022 ). Furthermore, ML can serve as a low-dimensional approach to replace CFD by using deep learning ( Yevtushenko et al, 2022 ; Yevtushenko et al, 2023 ).…”