Detection of preventable fetal distress during labor from scanned cardiotocogram tracings using deep learning

TL;DR

This paper introduces a deep learning framework that analyzes scanned cardiotocogram tracings to detect preventable fetal distress during labor, aiming to improve timely intervention and fetal outcomes.

Contribution

The study presents a novel deep learning approach trained on 50 years of EFM data, achieving high accuracy in early detection of fetal injury from scanned tracings.

Findings

94% accuracy in identifying early fetal injury

Effective automation of fetal well-being monitoring

Potential to guide timely clinical interventions

Abstract

Despite broad application during labor and delivery, there remains considerable debate about the value of electronic fetal monitoring (EFM). EFM includes the surveillance of the fetal heart rate (FHR) patterns in conjunction with the maternal uterine contractions providing a wealth of data about fetal behavior and the threat of diminished oxygenation and perfusion. Adverse outcomes universally associate a fetal injury with the failure to timely respond to FHR pattern information. Historically, the EFM data, stored digitally, are available only as rasterized pdf images for contemporary or historical discussion and examination. In reality, however, they are rarely reviewed systematically. Using a unique archive of EFM collected over 50 years of practice in conjunction with adverse outcomes, we present a deep learning framework for training and detection of incipient or past fetal injury. We report 94% accuracy in identifying early, preventable fetal injury intrapartum. This framework is suited for automating an early warning and decision support system for maintaining fetal well-being during the stresses of labor. Ultimately, such a system could enable a physician to timely respond during labor and prevent adverse outcomes. When adverse outcomes cannot be avoided, they can provide guidance to the early neuroprotective treatment of the newborn.