Patient-specific surrogate model to predict pelvic floor dynamics during vaginal delivery

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023698%3A_____%2F24%3AN0000017" target="_blank" >RIV/00023698:_____/24:N0000017 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11120/24:43927558 RIV/49777513:23640/24:43972971

Result on the web

<a href="https://pubmed.ncbi.nlm.nih.gov/39298872/" target="_blank" >https://pubmed.ncbi.nlm.nih.gov/39298872/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jmbbm.2024.106736" target="_blank" >10.1016/j.jmbbm.2024.106736</a>

Result language

angličtina

Original language name

Patient-specific surrogate model to predict pelvic floor dynamics during vaginal delivery

Original language description

Childbirth is a challenging event that can lead to long-term consequences such as prolapse or incontinence. While computational models are widely used to mimic vaginal delivery, their integration into clinical practice is hindered by time constraints. The primary goal of this study is to introduce an artificial intelligence pipeline that leverages patient-specific surrogate modeling to predict pelvic floor injuries during vaginal delivery. A finite element-based machine learning approach was implemented to generate a dataset with information from finite element simulations. Thousands of childbirth simulations were conducted, varying the dimensions of the pelvic floor muscles and the mechanical properties used for their characterization. Additionally, a mesh morphing algorithm was developed to obtain patient-specific models. Machine learning models, specifically tree-based algorithms such as Random Forest (RF) and Extreme Gradient Boosting, as well as Artificial Neural Networks, were trained to predict the nodal coordinates of nodes within the pelvic floor, aiming to predict the muscle stretch during a critical interval. The results indicate that the RF model performs best, with a mean absolute error (MAE) of 0.086 mm and a mean absolute percentage error of 0.38%. Overall, more than 80% of the nodes have an error smaller than 0.1 mm. The MAE for the calculated stretch is equal to 0.0011. The implemented pipeline allows loading the trained model and making predictions in less than 11 s. This work demonstrates the feasibility of implementing a machine learning framework in clinical practice to predict potential maternal injuries and assist in medical-decision making.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30214 - Obstetrics and gynaecology

Project

<a href="/en/project/EF17_048%2F0007280" target="_blank" >EF17_048/0007280: Application of Modern Technologies in Medicine and Industry</a><br>

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS

ISSN

1751-6161

e-ISSN

1878-0180

Volume of the periodical

160

Issue of the periodical within the volume

December

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

106736

UT code for WoS article

001319125400001

EID of the result in the Scopus database

2-s2.0-85203880448