Clinical Impact of Computational Heart Valve Models
Title
Clinical Impact of Computational Heart Valve Models
Date
2022
Publisher
MDPI AG
Subject
Aortic valve
Computational Analyses
Devices
Heart Valves
Mitral Valve
Pulmonary Valve
Repair
Tricuspid Valve
Computational Analyses
Devices
Heart Valves
Mitral Valve
Pulmonary Valve
Repair
Tricuspid Valve
Language
English
Abstract
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.
Source
Materials, Volume 15, Issue 9, May 2022, page 3302
Rights
© 2022 by the authors. 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/).
Format
PDF
Type
Text
Identifier
Bibliographic Citation
Toma M, Singh-Gryzbon S, Frankini E, Wei Z, Yoganathan AP. Clinical Impact of Computational Heart Valve Models. Materials. 2022; 15(9):3302. https://doi.org/10.3390/ma15093302
Files
Collection
Citation
Toma, M., Singh-Gryzbon, S., Frankini, E., Wei, Z. (Alan), & Yoganathan, A. P., Clinical Impact of Computational Heart Valve Models. Materials, Volume 15, Issue 9, May 2022, page 3302, New York Tech Institutional Repository, accessed October 11, 2024, https://repository.nyitlibrary.org/items/show/3729
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