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Clinical Impact of Computational Heart Valve Models

This paper provides a review of engineering applications and computational methods used to analyze the dynamics of heart valve closures in healthy and diseased states. Computational methods are a cost-effective tool that can be used to evaluate the flow parameters of heart valves. Valve repair and r...

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Autores principales: Toma, Milan, Singh-Gryzbon, Shelly, Frankini, Elisabeth, Wei, Zhenglun (Alan), Yoganathan, Ajit P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101262/
https://www.ncbi.nlm.nih.gov/pubmed/35591636
http://dx.doi.org/10.3390/ma15093302
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author Toma, Milan
Singh-Gryzbon, Shelly
Frankini, Elisabeth
Wei, Zhenglun (Alan)
Yoganathan, Ajit P.
author_facet Toma, Milan
Singh-Gryzbon, Shelly
Frankini, Elisabeth
Wei, Zhenglun (Alan)
Yoganathan, Ajit P.
author_sort Toma, Milan
collection PubMed
description This paper provides a review of engineering applications and computational methods used to analyze the dynamics of heart valve closures in healthy and diseased states. Computational methods are a cost-effective tool that can be used to evaluate the flow parameters of heart valves. Valve repair and replacement have long-term stability and biocompatibility issues, highlighting the need for a more robust method for resolving valvular disease. For example, while fluid–structure interaction analyses are still scarcely utilized to study aortic valves, computational fluid dynamics is used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress, and oscillatory shear index in the thoracic aorta. It has been analyzed that computational flow dynamic analyses can be integrated with other methods to create a superior, more compatible method of understanding risk and compatibility.
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spelling pubmed-91012622022-05-14 Clinical Impact of Computational Heart Valve Models Toma, Milan Singh-Gryzbon, Shelly Frankini, Elisabeth Wei, Zhenglun (Alan) Yoganathan, Ajit P. Materials (Basel) Review This paper provides a review of engineering applications and computational methods used to analyze the dynamics of heart valve closures in healthy and diseased states. Computational methods are a cost-effective tool that can be used to evaluate the flow parameters of heart valves. Valve repair and replacement have long-term stability and biocompatibility issues, highlighting the need for a more robust method for resolving valvular disease. For example, while fluid–structure interaction analyses are still scarcely utilized to study aortic valves, computational fluid dynamics is used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress, and oscillatory shear index in the thoracic aorta. It has been analyzed that computational flow dynamic analyses can be integrated with other methods to create a superior, more compatible method of understanding risk and compatibility. MDPI 2022-05-05 /pmc/articles/PMC9101262/ /pubmed/35591636 http://dx.doi.org/10.3390/ma15093302 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Toma, Milan
Singh-Gryzbon, Shelly
Frankini, Elisabeth
Wei, Zhenglun (Alan)
Yoganathan, Ajit P.
Clinical Impact of Computational Heart Valve Models
title Clinical Impact of Computational Heart Valve Models
title_full Clinical Impact of Computational Heart Valve Models
title_fullStr Clinical Impact of Computational Heart Valve Models
title_full_unstemmed Clinical Impact of Computational Heart Valve Models
title_short Clinical Impact of Computational Heart Valve Models
title_sort clinical impact of computational heart valve models
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9101262/
https://www.ncbi.nlm.nih.gov/pubmed/35591636
http://dx.doi.org/10.3390/ma15093302
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