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Wound contraction under negative pressure therapy measured with digital image correlation and finite-element analysis in tissue phantoms and wound models

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posted on 2021-11-11, 13:26 authored by Begum Zeybek, Simin LiSimin Li, Vadim SilberschmidtVadim Silberschmidt, Yang LiuYang Liu
The purpose of this study is to demonstrate the capabilities of finite-element (FE) models to predict contraction of wounds managed with negative pressure wound therapy (NPWT). The features of wounds and surrounding tissues were analysed to gain insights into the mechanical effects of NPWT on them. 3D digital image correlation (DIC) measurement of soft tissue phantoms was used to investigate the effect of wound thickness, size, and shape, which were further compared with results of FE simulations. It was noticed that with an increased NP level the difference between DIC and FE in wound contraction became more pronounced, particularly for the thick wounds. In addition, the locations of the wounds were evaluated to predict their contraction characteristics, based on surrounding tissue structures, in 3D using the developed FE models. It was demonstrated that features and location of wounds influenced their deformations differently for the same pressure levels. Overall, this study, involving a combined experimental and computational approach, allowed the important insights into mechanical effects of NPWT.

Funding

Loughborough University, Wolfson School of Mechanical, Electrical and Manufacturing Engineering

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Medical Engineering & Physics

Volume

98

Pages

104 - 114

Publisher

Elsevier BV

Version

  • AM (Accepted Manuscript)

Rights holder

© IPEM

Publisher statement

This paper was accepted for publication in the journal Medical Engineering & Physics and the definitive published version is available at https://doi.org/10.1016/j.medengphy.2021.11.003

Acceptance date

2021-11-02

Publication date

2021-11-06

Copyright date

2021

ISSN

1350-4533

Language

  • en

Depositor

Dr Yang Liu. Deposit date: 11 November 2021

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