Simulation of Mechanical Heart Valve Dysfunction and the Non-Newtonian Blood Model Approach

Chen, Aolin; Basri, Adi Azriff; Ismail, Nor Atiah; Tamagawa, Masaaki; Zhu, Di; Ahmad, Kamarul Arifin

doi:10.1155/2022/9612296

Cited by 5 publications

(4 citation statements)

References 138 publications

(235 reference statements)

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“…This is precisely why numerical simulation has emerged as an essential foundation for such optimization, becoming a crucial element in the advancement of medical device research ( Bologna et al, 2023 ) and the assessment of stress-strain patterns in specific pathological conditions within the cardiovascular system ( Di Giuseppe et al, 2021 ). FEM is particularly advanced among numerical simulation tools and has been demonstrated to be highly efficient in modeling PHVs ( Borazjani, 2013 ; Gilmanov and Sotiropoulos, 2016 ; Castravete et al, 2020 ; Lee et al, 2020 ; Chen et al, 2022 ). Furthermore, existing research has demonstrated advanced optimization algorithms that enable the semi-automatic generation and FEM analysis of significant quantities of PHVs, facilitating the selection of optimal candidates among them ( Hsu et al, 2015 ; Li and Sun, 2017 ; Abbasi and Azadani, 2020 ; Gulbulak et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization

Danilov,

Klyshnikov,

Onishenko

et al. 2023

Front. Bioeng. Biotechnol.

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Majority of modern techniques for creating and optimizing the geometry of medical devices are based on a combination of computer-aided designs and the utility of the finite element method This approach, however, is limited by the number of geometries that can be investigated and by the time required for design optimization. To address this issue, we propose a generative design approach that combines machine learning (ML) methods and optimization algorithms. We evaluate eight different machine learning methods, including decision tree-based and boosting algorithms, neural networks, and ensembles. For optimal design, we investigate six state-of-the-art optimization algorithms, including Random Search, Tree-structured Parzen Estimator, CMA-ES-based algorithm, Nondominated Sorting Genetic Algorithm, Multiobjective Tree-structured Parzen Estimator, and Quasi-Monte Carlo Algorithm. In our study, we apply the proposed approach to study the generative design of a prosthetic heart valve (PHV). The design constraints of the prosthetic heart valve, including spatial requirements, materials, and manufacturing methods, are used as inputs, and the proposed approach produces a final design and a corresponding score to determine if the design is effective. Extensive testing leads to the conclusion that utilizing a combination of ensemble methods in conjunction with a Tree-structured Parzen Estimator or a Nondominated Sorting Genetic Algorithm is the most effective method in generating new designs with a relatively low error rate. Specifically, the Mean Absolute Percentage Error was found to be 11.8% and 10.2% for lumen and peak stress prediction respectively. Furthermore, it was observed that both optimization techniques result in design scores of approximately 95%. From both a scientific and applied perspective, this approach aims to select the most efficient geometry with given input parameters, which can then be prototyped and used for subsequent in vitro experiments. By proposing this approach, we believe it will replace or complement CAD-FEM-based modeling, thereby accelerating the design process and finding better designs within given constraints. The repository, which contains the essential components of the study, including curated source code, dataset, and trained models, is publicly available at https://github.com/ViacheslavDanilov/generative_design.

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Section: Discussionmentioning

confidence: 99%

Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization

Danilov,

Klyshnikov,

Onishenko

et al. 2023

Front. Bioeng. Biotechnol.

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“…Tese investigations have revealed that the Carreau non-Newtonian model is better at predicting these parameters. Terefore, the Carreau model has been suggested as the most suitable model for blood in various studies [7,14] and will also be used in this study.…”

Section: Introductionmentioning

confidence: 99%

The Role of Wall Mechanics in the Hemodynamics of a Realistic Abdominal Aortic Aneurysm: A Fluid-Structure Interaction Study

Moradicheghamahi

2024

Journal of Engineering

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Abdominal aortic aneurysm (AAA) can lead to high mortality rates and further complications such as stroke or heart attack due to the risk of rupture and thrombosis. Wall mechanics play a crucial role in the development and progression of aneurysms. This study investigated the effects of wall mechanics on hemodynamic parameters in AAA to understand the risk of rupture and thrombosis. The impact of three aortic wall models (rigid, linear elastic, and hyperelastic) on structural and hemodynamic parameters was examined using CFD and FSI techniques. The blood was modeled using the Carreau non-Newtonian model, and the flow was simulated using the k-ω model. Physiological pulses were used for the velocity at the inlet and the pressure at the outlet. The results demonstrated close similarity between the predictions of the linear elastic and hyperelastic models, in contrast to the somewhat different results of the rigid model. The hyperelastic model predicted higher deformation and von Mises stress levels than the elastic model, although the difference in stress predictions was smaller than the difference in deformation predictions. The rigid model evaluated the time-averaged wall shear stress and oscillatory shear index higher than the other two models in the aneurysmal area but with a lower relative residence time. In general, the hyperelastic model predicted a higher risk of rupture than linear elastic models and a higher risk of thrombus formation than the other two models. The rigid model had the most optimistic prediction.

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“…By examining the OSI contour for different rheological models, it has been demonstrated that the Carreau non-Newtonian model has a better prediction for OSI and its maximum value [9]. The Carreau model has been proposed as the most appropriate model for blood in several studies [9,30,31], which will also be used in this study.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Effects of an Inferior Vena Cava Filter and Captured Clot Size on the Hemodynamic Parameters in Non-Newtonian Turbulent Pulsatile Blood Flow

Moradicheghamahi,

Qasim,

Jafarpour

et al. 2023

Applied Bionics and Biomechanics

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In this computational fluid dynamics (CFD)-based study, the effects of inferior vena cava (IVC) filter implantation on the risk of IVC thrombosis have been investigated using different hemodynamic parameters, including time-averaged wall shear stress (TAWSS), the oscillating shear index (OSI), and the relative residence time (RRT). The boundary conditions in this study have been based on physiological pulses. Additionally, the k–ω model and the Carreau model have been chosen to represent the turbulent flow regime and non-Newtonian blood, respectively. For this purpose, three blood clots with the largest cross-sectional diameters of 30%, 50%, and 70% of the filter diameter have been used. Capturing a small clot in the filter has the minimum effect on the hemodynamic parameters, while by increasing the size of the captured clot, OSI and RRT parameters increase in areas downstream of the filter on the wall. The presence of a filter and clot increases the risk of thrombosis. In the case of capturing large clots, there is the possibility of damage to endothelial cells or platelet activation. Captured clots lead to the formation of plaque and thrombus on the IVC wall. However, the possibility of thrombus growth on its surface is not negligible, particularly if larger clots are trapped in the filter.

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Simulation of Mechanical Heart Valve Dysfunction and the Non-Newtonian Blood Model Approach

Cited by 5 publications

References 138 publications

Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization

Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization

The Role of Wall Mechanics in the Hemodynamics of a Realistic Abdominal Aortic Aneurysm: A Fluid-Structure Interaction Study

Investigation of the Effects of an Inferior Vena Cava Filter and Captured Clot Size on the Hemodynamic Parameters in Non-Newtonian Turbulent Pulsatile Blood Flow

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