Prediction of uterine dehiscence via machine learning by using lower uterine segment thickness and clinical features.

ABSTRACT:

Background

With the global increase of cesarean delivery rates, the long-term effects of cesarean delivery have started to become clearer. One of the most prominent complications of cesarean delivery in recurrent pregnancies is uterine rupture. Assessing the risk of uterine rupture by accurately predicting dehiscence is very important to prevent untimely operations and/or maternal and fetal complications.

Objective

This study aimed to assess whether machine learning can be used to predict uterine dehiscence by using patients' ultrasonographic findings, clinical findings, and demographic data as features. Hence, we investigated a potential method for preventing uterine rupture and its maternal and fetal complications.

Study design

The study was conducted on 317 patient

SUBMITTER: Kement M 

PROVIDER: S-EPMC9758335 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature