Development and validation of a machine-learning model for prediction of shoulder dystocia.

Journal: Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
Published Date: Oct 1, 2020

Abstract

OBJECTIVES: To develop a machine-learning (ML) model for prediction of shoulder dystocia (ShD) and to externally validate the model's predictive accuracy and potential clinical efficacy in optimizing the use of Cesarean delivery in the context of suspected macrosomia.

Authors

  • A Tsur
    Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • L Batsry
    Department of Obstetrics and Gynecology, The Sheba Medical Center, Tel Hashomer, Israel.
  • S Toussia-Cohen
    Department of Obstetrics and Gynecology, The Sheba Medical Center, Tel Hashomer, Israel.
  • M G Rosenstein
    Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, University of California, San Francisco, CA, USA.
  • O Barak
    Department of Obstetrics and Gynecology, The Kaplan Medical Center, Rehovot, Israel.
  • Y Brezinov
    Department of Obstetrics and Gynecology, The Kaplan Medical Center, Rehovot, Israel.
  • R Yoeli-Ullman
    Department of Obstetrics and Gynecology, The Sheba Medical Center, Tel Hashomer, Israel.
  • E Sivan
    Department of Obstetrics and Gynecology, The Sheba Medical Center, Tel Hashomer, Israel.
  • M Sirota
    Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
  • M L Druzin
    Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • D K Stevenson
    Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Y J Blumenfeld
    Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • D Aran
    Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.

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