A machine learning approach to predict in vivo skin growth

TL;DR

This paper introduces a machine learning method to predict skin growth during expansion using non-invasive measurements, improving reconstructive surgery outcomes.

Contribution

A novel machine learning approach is developed to predict in vivo skin growth and properties using simulated data and neural networks.

Findings

An artificial neural network accurately predicted future skin growth with an average R2 of 0.9353.

The model also predicted subject-specific properties like shear modulus and growth rate with high accuracy.

The method could enable real-time prediction of skin expansion outcomes for personalized protocols.

Abstract

Since their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Currently, however, the sole quantitative method to assess skin growth requires skin excision. Thus, in the context of patient outcomes, a machine learning method which uses non-invasive measurements to predict in vivo skin growth and other skin properties, holds significant value. In this study, the finite element method was used to simulate a typical skin expansion protocol and to perform various simulated wave propagation experiments during the first few days of expansion on 1,000 individual virtual subjects. An artificial neural network trained on this dataset was shown to be capable of predicting the future skin growth at 7 days (avg. R2
= 0.9353) as well as the subject-specific shear modulus (R2
= 0.9801), growth rate (R2
= 0.8649), and natural pre-stretch (R2
= 0.9783) with a very high degree of accuracy. The method presented here has implications for the real-time prediction of patient-specific skin expansion outcomes and could facilitate the development of patient-specific protocols.

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