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Title: | High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations | Authors: | EGBERTS, Ginger VERMOLEN, Fred van Zuijlen, Paul |
Issue Date: | 2023 | Publisher: | FRONTIERS MEDIA SA | Source: | Frontiers in applied mathematics and statistics, 9 (Art N° 1098242) | Abstract: | Severe burn injuries often lead to skin contraction, leading to stresses in and around the damaged skin region. If this contraction leads to impaired joint mobility, one speaks of contracture. To optimize treatment, a mathematical model, that is based on finite element methods, is developed. Since the finite element-based simulation of skin contraction can be expensive from a computational point of view, we use machine learning to replace these simulations such that we have a cheap alternative. The current study deals with a feed-forward neural network that we trained with 2D finite element simulations based on morphoelasticity. We focus on the evolution of the scar shape, wound area, and total strain energy, a measure of discomfort, over time. The results show average goodness of fit (R-2) of 0.9979 and a tremendous speedup of 1815000X. Further, we illustrate the applicability of the neural network in an online medical app that takes the patient's age into account. | Notes: | Egberts, G (corresponding author), Delft Univ Technol, Delft Inst Appl Math, Delft, Netherlands.; Egberts, G (corresponding author), Univ Hasselt, Dept Math & Stat, Res Grp Computat Math CMAT, Hasselt, Belgium. egberts.ginger@gmail.com |
Keywords: | machine learning;post-burn scar contraction;morphoelasticity;feed-forward neural network;online application;Monte Carlo simulations | Document URI: | http://hdl.handle.net/1942/39652 | e-ISSN: | 2297-4687 | DOI: | 10.3389/fams.2023.1098242 | ISI #: | 000931107400001 | Rights: | 2023 Egberts, Vermolen and van Zuijlen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations.pdf | Published version | 1.72 MB | Adobe PDF | View/Open |
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