A novel approach based on machine learning analysis of flow velocity waveforms to identify unseen abnormalities of the umbilical cord.

Journal: Placenta
Published Date: Sep 1, 2022

Abstract

INTRODUCTION: A Doppler ultrasound (DUS) is essential for detecting blood flow abnormalities in the umbilical cord (UC). Any morphological abnormalities of the UC may lead to morbidity and stillbirth. Some abnormalities such as torsion, strictures and true-knot, however, may only be discovered at birth. This study proposes a novel approach of using machine learning analysis of flow velocity waveforms to improve the diagnosis of UC abnormalities.

Authors

  • Sara Naftali
    School of Medical Engineering, Afeka - Tel Aviv Academic College of Engineering, 38 Mivtza Kadesh St., Tel Aviv, 6998812, Israel. Electronic address: saran@afeka.ac.il.
  • Yuval Nareznoy Ashkenazi
    School of Medical Engineering, Afeka - Tel Aviv Academic College of Engineering, 38 Mivtza Kadesh St., Tel Aviv, 6998812, Israel; Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel.
  • Anat Ratnovsky
    School of Medical Engineering, Afeka - Tel Aviv Academic College of Engineering, 38 Mivtza Kadesh St., Tel Aviv, 6998812, Israel.

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