Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/36642
Title: A Bayesian finite-element trained machine learning approach for predicting post-burn contraction
Authors: EGBERTS, Ginger 
Schaaphok, Marianne
VERMOLEN, Fred 
van Zuijlen, Paul
Issue Date: 2022
Publisher: SPRINGER LONDON LTD
Source: NEURAL COMPUTING & APPLICATIONS, 34 (11) , p. 8635-8642
Abstract: Burn injuries can decrease the quality of life of a patient tremendously, because of esthetic reasons and because of contractions that result from them. In severe case, skin contraction takes place at such a large extent that joint mobility of a patient is significantly inhibited. In these cases, one refers to a contracture. In order to predict the evolution of post-wounding skin, several mathematical model frameworks have been set up. These frameworks are based on complicated systems of partial differential equations that need finite element-like discretizations for the approximation of the solution. Since these computational frameworks can be expensive in terms of computation time and resources, we study the applicability of neural networks to reproduce the finite element results. Our neural network is able to simulate the evolution of skin in terms of contraction for over one year. The simulations are based on 25 input parameters that are characteristic for the patient and the injury. One of such input parameters is the stiffness of the skin. The neural network results have yielded an average goodness of fit (R-2) of 0.9928 (+/- 0.0013). Further, a tremendous speed-up of 19354X was obtained with the neural network. We illustrate the applicability by an online medical App that takes into account the age of the patient and the length of the burn.
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.
G.Egberts@tudelft.nl; fred.vermolen@uhasselt.be
Keywords: Machine learning;Post-burn scar contraction;Morphoelasticity;Feed-forward neural network;Medical application;Monte Carlo simulations
Document URI: http://hdl.handle.net/1942/36642
ISSN: 0941-0643
e-ISSN: 1433-3058
DOI: 10.1007/s00521-021-06772-3
ISI #: 000750866400002
Rights: The Author(s) 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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