A self-contact electromechanical framework for intestinal motility

TL;DR

This paper presents a novel multiphysics electromechanical model incorporating contact mechanics to simulate intestinal motility, capturing complex behaviors like hernias and adhesions with potential clinical relevance.

Contribution

It introduces an innovative generalized framework combining electromechanical coupling, contact detection, and boundary conditions for intestinal motility modeling.

Findings

Model reproduces propagative contractions in complex scenarios

Low peristalsis observed in pre-strangulation zones

High pressure recorded in strangulation zones

Abstract

This study introduces an advanced multiphysics and multiscale modeling approach to investigate intestinal motility. We propose a generalized electromechanical framework that incorporates contact mechanics, enabling the development of a unique and innovative model for intestinal motility. The theoretical framework includes an electromechanical model coupling a microstructural material model, which describes the intestinal structure, with an electrophysiological model that captures the propagation of slow waves. Additionally, it integrates a self-contact detection algorithm based on a nearest-neighbour search and the penalty method, along with boundary conditions that account for the influence of surrounding organs. A staggered finite element scheme implemented in FEniCS is employed to solve the governing equations using the finite element method. The model is applied to study cases of moderate and severe strangulation hernia, as well as intestinal adhesion syndrome. The results demonstrate that low peristalsis takes place in the pre-strangulation zone. At the same time, very high pressure is recorded in the strangulation zone, and peristaltic contractions persisted in the healthy region. For adhesions, the results indicate a complete absence of peristalsis in the adherent region. The model successfully reproduces both qualitatively and quantitatively propagative contractions in complex scenarios, such as pre- and post-surgical conditions, thereby highlighting its potential to provide valuable insights for clinical applications.