Development and validation of a machine learning model for prediction of cephalic dystocia.

ABSTRACT:

Background

Early detection of cephalic dystocia is challenging, and current clinical assessment tools are limited. Machine learning offers unique advantages, enabling the generation of predictive models using various types of clinical data. Our model aims to integrate objective ultrasound data with psychological and sociological characteristics and obstetric treatment data to predict the individual probability of cephalic dystocia in pregnant women.

Methods

We collected data from 302 pregnant women who underwent examinations and deliveries at Southern Medical University's Nanfang Hospital from January 2022 to December 2023. We utilized basic patient characteristics, foetal ultrasound parameters, maternal anthropometric data, maternal psychological measurements, and obstetric

SUBMITTER: Huang Y 

PROVIDER: S-EPMC12363056 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature