Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix.

Journal: Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
Published Date: Jul 1, 2019

Abstract

OBJECTIVE: To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine-learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL).

Authors

  • R O Bahado-Singh
    Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA.
  • J Sonek
    Division of Maternal Fetal Medicine, Wright State University, Dayton, OH, USA.
  • D McKenna
    Department of Obstetrics and Gynecology, Miami Valley Hospital South, Tampa, FL, USA.
  • D Cool
    Department of Pharmacology and Toxicology, Wright State University, Dayton, OH, USA.
  • B Aydas
    Department of Computer Science, Albion College, Albion, MI, USA.
  • O Turkoglu
    Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA.
  • T Bjorndahl
    Department of Biological Science, University of Alberta, Edmonton, AB, Canada.
  • R Mandal
    Department of Biological Science, University of Alberta, Edmonton, AB, Canada.
  • D Wishart
    Department of Biological Science, University of Alberta, Edmonton, AB, Canada.
  • P Friedman
    Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA.
  • S F Graham
    Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA.
  • A Yilmaz
    Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA.

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