ArticleMedical & biological engineering & computing2024
On the uncertainty quantification of the active uterine contraction during the second stage of labor simulation.
Article in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Advanced FE simulation coupled with statistical surrogate modeling toward a multifactorial view on the pelvic floor muscle damage and perineal tearing during childbirth.Medical & biological engineering & computing · 2026Article
- What do we actually know about the biomechanics of pregnancy and labour? A systematic scoping review.PloS one · 2025Article
- Finite element modeling in obstetrics and gynecology: advances, applications, and challenges.Frontiers in medicine · 2025Review
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 (
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