Artificial intelligence-based prediction of second stage duration in labor: a multicenter retrospective cohort analysis.

ABSTRACT:

Background

Duration of second stage of labor is crucial for fetal delivery, but the optimal length of this stage remains controversial. While extending the duration of second stage can reduce primary cesarean delivery rates, it may increase maternal and neonatal morbidities as the duration progresses. We aimed to develop a personalized machine learning (ML) model to predict the possible second-stage duration.

Methods

This multicenter, retrospective study was conducted at four tertiary hospitals in China from September 2013 to October 2022. Data from three hospitals in Guangdong Province was selected as derivation set, and a geographically independent dataset from Fujian Province as the external validation set. Singleton vaginal deliveries with term live birth in a cephalic p

SUBMITTER: Huang X 

PROVIDER: S-EPMC11831126 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature