Concentration‐specific constitutive modeling of gelatin based on artificial neural networks

Gelatin phantoms are frequently used in the development of surgical devices and medical imaging techniques. They exhibit mechanical properties similar to soft biological tissues [1] but can be handled at a much lower cost. Moreover, they enable a better reproducibility of experiments. Accurate constitutive models for gelatin are therefore of great interest for biomedical engineering. In particular it is important to capture the dependence of mechanical properties of gelatin on its concentration. Herein we propose a simple machine learning approach to this end. It uses artiﬁcial neural networks (ANN) for learning from indentation data the relation between the concentration of ballistic gelatin and the resulting mechanical properties.
© 2021 The Authors Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH

DDC Class

570: Biowissenschaften, Biologie

600: Technik

610: Medizin

620: Ingenieurwissenschaften

Funding(s)

I³-Lab - Modell-gestütztes maschinelles Lernen für die Weichgewebsmodellierung in der Medizin

Projekt DEAL

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The authors greatfully acknowledge financial support from Hamburg University of Technology (TUHH) within the I3-Lab ‘Modell-gestütztes maschinelles Lernen für die Weichgewebsmodellierung in der Medizin’.

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Concentration‐specific constitutive modeling of gelatin based on artificial neural networks