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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30214 - Obstetrics and gynaecology

Projekt

<a href="/cs/project/EF17_048%2F0007280" target="_blank" >EF17_048/0007280: Aplikace moderních technologií v medicíně a průmyslu</a><br>

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS

ISSN

1751-6161

e-ISSN

1878-0180

Svazek periodika

160

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

106736

Kód UT WoS článku

001319125400001

EID výsledku v databázi Scopus

2-s2.0-85203880448