Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.

Journal: PloS one
PMID: 35731736

Abstract

OBJECTIVE: To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester.

Authors

  • Mark C Walker
    Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada.
  • Inbal Willner
    Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada.
  • Olivier X Miguel
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.
  • Malia S Q Murphy
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Darine El-Chaâr
    Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada.
  • Felipe Moretti
    Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada.
  • Alysha L J Dingwall Harvey
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.
  • Ruth Rennicks White
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada.
  • Katherine A Muldoon
    Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, Canada.
  • André M Carrington
    Ottawa Hospital Research Institute, Ottawa, K1H 8L6, Canada. acarrington@ohri.ca.
  • Steven Hawken
    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Richard I Aviv
    Department of Radiology and Medical Imaging, University of Ottawa, Ottawa, Canada.

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