Detection of Preventable Fetal Distress During Labor From Scanned Cardiotocogram Tracings Using Deep Learning

Abstract

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Despite broad application during labor and delivery, there remains considerable debate about the value of electronic fetal monitoring (EFM). EFM includes the surveillance of fetal heart rate (FHR) patterns in conjunction with the mother's uterine contractions, providing a wealth of data about fetal behavior and the threat of diminished oxygenation and cerebral 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 or purposefully. 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 obstetrical care providers to timely respond during labor and prevent both urgent intervention and adverse outcomes. When adverse outcomes cannot be avoided, they can provide guidance to the early neuroprotective treatment of the newborn.

Keywords

• cardiotocography
• deep learning-artificial neural network (DL-ANN)
• fetal brain injury
• convolutional neural network (CNN)
• prevention