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

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 ne

SUBMITTER: Egberts G 

PROVIDER: S-EPMC8801043 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature