Detection of preventable fetal distress during labor from scanned cardiotocogram tracings using deep learning
release_xmx4qcvzdfabpkqd4mca55ifcy
by
Martin G. Frasch, Shadrian B. Strong, David Nilosek, Joshua Leaverton, Barry S. Schifrin
2021
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.
In text/plain
format
Archived Files and Locations
application/pdf 419.0 kB
file_hjpkwrsd7zbe7h5mervt3jcsta
|
arxiv.org (repository) web.archive.org (webarchive) |
2106.00628v1
access all versions, variants, and formats of this works (eg, pre-prints)