Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning.

ABSTRACT:

Background

Electronic fetal monitoring is used in most US hospital births but has significant limitations in achieving its intended goal of preventing intrapartum hypoxic-ischemic injury. Novel deep learning techniques can improve complex data processing and pattern recognition in medicine.

Objective

This study aimed to apply deep learning approaches to develop and validate a model to predict fetal acidemia from electronic fetal monitoring data.

Study design

The database was created using intrapartum electronic fetal monitoring data from 2006 to 2020 from a large, multisite academic health system. Data were divided into training and testing sets with equal distribution of acidemic cases. Several different deep learning architectures were explored. The primary outcome

SUBMITTER: McCoy JA 

PROVIDER: S-EPMC11499302 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature