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http://hdl.handle.net/1942/39652
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DC Field | Value | Language |
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dc.contributor.author | EGBERTS, Ginger | - |
dc.contributor.author | VERMOLEN, Fred | - |
dc.contributor.author | van Zuijlen, Paul | - |
dc.date.accessioned | 2023-03-08T13:20:47Z | - |
dc.date.available | 2023-03-08T13:20:47Z | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-03-03T15:07:09Z | - |
dc.identifier.citation | Frontiers in applied mathematics and statistics, 9 (Art N° 1098242) | - |
dc.identifier.uri | http://hdl.handle.net/1942/39652 | - |
dc.description.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. | - |
dc.description.sponsorship | This study was supported by Dutch Burns Foundation, Project 17.105. The authors are grateful for the financial support by the Dutch Burns Foundation under Project 17.105. | - |
dc.language.iso | en | - |
dc.publisher | FRONTIERS MEDIA SA | - |
dc.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. | - |
dc.subject.other | machine learning | - |
dc.subject.other | post-burn scar contraction | - |
dc.subject.other | morphoelasticity | - |
dc.subject.other | feed-forward neural network | - |
dc.subject.other | online application | - |
dc.subject.other | Monte Carlo simulations | - |
dc.title | High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations | - |
dc.type | Journal Contribution | - |
dc.identifier.volume | 9 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.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. | - |
dc.description.notes | egberts.ginger@gmail.com | - |
local.publisher.place | AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 1098242 | - |
dc.identifier.doi | 10.3389/fams.2023.1098242 | - |
dc.identifier.isi | 000931107400001 | - |
dc.identifier.eissn | - | |
local.provider.type | wosris | - |
local.description.affiliation | [Egberts, Ginger] Delft Univ Technol, Delft Inst Appl Math, Delft, Netherlands. | - |
local.description.affiliation | [Egberts, Ginger; Vermolen, Fred] Univ Hasselt, Dept Math & Stat, Res Grp Computat Math CMAT, Hasselt, Belgium. | - |
local.description.affiliation | [van Zuijlen, Paul] Red Cross Hosp, Burn Ctr, Beverwijk, Netherlands. | - |
local.description.affiliation | [van Zuijlen, Paul] Red Cross Hosp, Dept Plast Reconstruct & Hand Surg, Beverwijk, Netherlands. | - |
local.description.affiliation | [van Zuijlen, Paul] Amsterdam UMC, Dept Plast Reconstruct & Hand Surg, Locat VUmc, Amsterdam Movement Sci, Amsterdam, Netherlands. | - |
local.description.affiliation | [van Zuijlen, Paul] Amsterdam UMC, Emma Childrens Hosp, Pediat Surg Ctr, Locat AMC, Amsterdam, Netherlands. | - |
local.description.affiliation | [van Zuijlen, Paul] Vrije Univ Amsterdam Med Ctr, Amsterdam, Netherlands. | - |
local.uhasselt.international | yes | - |
item.contributor | EGBERTS, Ginger | - |
item.contributor | VERMOLEN, Fred | - |
item.contributor | van Zuijlen, Paul | - |
item.fullcitation | EGBERTS, Ginger; VERMOLEN, Fred & van Zuijlen, Paul (2023) High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations. In: Frontiers in applied mathematics and statistics, 9 (Art N° 1098242). | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
crisitem.journal.eissn | 2297-4687 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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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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