A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

Ginger Egberts*, Marianne Schaaphok, Fred Vermolen, Paul van Zuijlen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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 (R2) 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.

Original languageEnglish
Pages (from-to)8635-8642
Number of pages8
JournalNeural Computing and Applications
Volume34
Issue number11
DOIs
Publication statusPublished - 2022

Keywords

  • Feed-forward neural network
  • Machine learning
  • Medical application
  • Monte Carlo simulations
  • Morphoelasticity
  • Post-burn scar contraction

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