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Atrial Constitutive Neural Networks

Conference Paper (2025)
Author(s)

M. Peirlinck (TU Delft - Medical Instruments & Bio-Inspired Technology)

Kevin Linka (RWTH Aachen University)

E. Kuhl (Stanford University)

Research Group
Medical Instruments & Bio-Inspired Technology
DOI related publication
https://doi.org/10.1007/978-3-031-94559-5_23
More Info
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Publication Year
2025
Language
English
Research Group
Medical Instruments & Bio-Inspired Technology
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
249-259
ISBN (print)
978-3-031-94558-8
ISBN (electronic)
978-3-031-94559-5
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health.

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File under embargo until 29-11-2025