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

Journal: American journal of obstetrics and gynecology
PMID: 38663662

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.

Authors

  • Jennifer A McCoy
    Maternal Fetal Medicine Research Program, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. Electronic address: Jennifer.mccoy@pennmedicine.upenn.edu.
  • Lisa D Levine
    Maternal Fetal Medicine Research Program, Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • Guangya Wan
    School of Data Science, University of Virginia, Charlottesville, VA.
  • Corey Chivers
    Penn Medicine, University of Pennsylvania, Philadelphia.
  • Joseph Teel
    Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
  • William G La Cava
    Computational Health Informatics Program (W.G.L.C.), Boston Children's Hospital, MA.

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