# Optimization of the cut configuration for skin grafts

## TL;DR

This paper explores how to optimize skin graft cut configurations using mechanical models to improve surgical efficiency.

## Contribution

The novel contribution is combining gradient descent with genetic algorithms to optimize skin graft cut configurations under mechanical stress.

## Key findings

- Gradient descent performs well under uniaxial stretching but is limited in multidirectional cases.
- Combining genetic algorithms with gradient descent significantly improves optimization results.
- Existence of solutions is proven, but uniqueness cannot be guaranteed.

## Abstract

The subject of this work is the problem of optimizing the configuration of cuts for skin grafting in order to improve the efficiency of the procedure. We consider the optimization problem in the framework of a linear elasticity model. We choose three mechanical measures that define optimality via related objective functionals: the compliance, the \documentclass[12pt]{minimal}
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\begin{document}$$L^p$$\end{document}Lp-norm of the von Mises stress, and the area covered by the stretched skin. We provide a proof of the existence of the solution for each problem, but we cannot claim uniqueness. We compute the gradient of the objectives with respect to the cut configuration using concepts from shape calculus. To solve the problem numerically, we apply the gradient descent method, which performs well under uniaxial stretching. However, in more complex cases, such as multidirectional stretching, its effectiveness is limited due to the low sensitivity of the functionals under consideration.To avoid this difficulty, we use a combination of the genetic algorithm and the gradient descent method, which leads to a significant improvement in the results.