Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor.

Journal: Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
PMID: 34309926

Abstract

OBJECTIVES: To describe a newly developed machine-learning (ML) algorithm for the automatic recognition of fetal head position using transperineal ultrasound (TPU) during the second stage of labor and to describe its performance in differentiating between occiput anterior (OA) and non-OA positions.

Authors

  • T Ghi
    Department of Medicine and Surgery, Obstetrics and Gynecology Unit, University of Parma, Parma, Italy.
  • F Conversano
    National Research Council, Institute of Clinical Physiology, Lecce, Italy.
  • R Ramirez Zegarra
    Department of Medicine and Surgery, Obstetrics and Gynecology Unit, University of Parma, Parma, Italy.
  • P Pisani
    National Research Council, Institute of Clinical Physiology, Lecce, Italy.
  • A Dall'Asta
    Department of Medicine and Surgery, Obstetrics and Gynecology Unit, University of Parma, Parma, Italy.
  • A Lanzone
    Obstetrics and High-Risk Unit, Fondazione Policlinico A. Gemelli IRCCS, Rome, Italy.
  • W Lau
    Department of Obstetrics and Gynecology, Kwong Wah Hospital, Kowloon, Hong Kong.
  • A Vimercati
    Department of Obstetrics, Gynecology, Neonatology and Anesthesiology, University Hospital of Bari Consorziale Policlinico, Bari, Italy.
  • D G Iliescu
    University Emergency County Hospital, Craiova, Romania.
  • I Mappa
    Division of Maternal and Fetal Medicine, Cristo Re Hospital, University of Rome Tor Vergata, Rome, Italy.
  • G Rizzo
    Division of Maternal and Fetal Medicine, Cristo Re Hospital, University of Rome Tor Vergata, Rome, Italy.
  • S Casciaro
    National Research Council, Institute of Clinical Physiology, Lecce, Italy.

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