High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations - Egberts, Ginger; Vermolen, Fred; van Zuijlen, Paul (Frontiers in Applied Mathematics and Statistics, 2023)
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 study the use of machine learning to reproduce the expensive finite element simulations cheaply. 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 of 99.79% 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.