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Updated: Jul 1, 2025

Author Spotlight: Advancing Labor Management Through Electromyometrial Imaging for Understanding Uterine Contractions
Published on: May 26, 2023
On the uncertainty quantification of the active uterine contraction during the second stage of labor simulation
Trieu-Nhat-Thanh Nguyen1, Abbass Ballit1, Pauline Lecomte-Grosbras1
1Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59000, Lille, France.
Abstract:
Uterine contractions in the myometrium occur at multiple scales, spanning both organ and cellular levels. This complex biological process plays an essential role in the fetus delivery during the second stage of labor. Several finite element models of active uterine contractions have already been developed to simulate the descent of the fetus through the birth canal. However, the developed models suffer severe reliability issues due to the uncertain parameters. In this context, the present study aimed to perform the uncertainty quantification (UQ) of the active uterine contraction simulation to advance our understanding of pregnancy mechanisms with more reliable indicators. A uterus model with and without fetus was developed integrating a transversely isotropic Mooney-Rivlin material with two distinct fiber orientation architectures. Different contraction patterns with complex boundary conditions were designed and applied. A global sensitivity study was performed to select the most valuable parameters for the uncertainty quantification (UQ) process using a copula-based Monte Carlo method. As results, four critical material parameters ( ) of the active uterine contraction model were identified and used for the UQ process. The stress distribution on the uterus during the fetus descent, considering first and second fiber orientation families, ranged from 0.144 to 1.234 MPa and 0.044 to 1.619 MPa, respectively. The simulation outcomes revealed also the segment-specific contraction pattern of the uterus tissue. The present study quantified, for the first time, the effect of uncertain parameters of the complex constitutive model of the active uterine contraction on the fetus descent process. As perspectives, a full maternal pelvis model will be coupled with reinforcement learning to automatically identify the delivery mechanism behind the cardinal movements of the fetus during the active expulsion process.
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