Deep-learning computer vision can identify increased nuchal translucency in the first trimester of pregnancy.

Journal: Prenatal diagnosis
PMID: 38558081

Abstract

OBJECTIVE: Many fetal anomalies can already be diagnosed by ultrasound in the first trimester of pregnancy. Unfortunately, in clinical practice, detection rates for anomalies in early pregnancy remain low. Our aim was to use an automated image segmentation algorithm to detect one of the most common fetal anomalies: a thickened nuchal translucency (NT), which is a marker for genetic and structural anomalies.

Authors

  • Bhavya Kasera
    Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
  • Shiri Shinar
    Department of Obstetrics and Gynaecology, Fetal Medicine Unit, Mount Sinai Hospital and University of Toronto, Toronto, Ontario, Canada.
  • Parinita Edke
    Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
  • Vagisha Pruthi
    Department of Obstetrics and Gynaecology, Fetal Medicine Unit, Mount Sinai Hospital and University of Toronto, Toronto, Ontario, Canada.
  • Anna Goldenberg
    SickKids Research Institute, 686 Bay Street, Toronto, ON M5G 0A4, Canada; Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4, Canada. Electronic address: anna.goldenberg@utoronto.ca.
  • Lauren Erdman
    Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada.
  • Tim Van Mieghem
    Department of Obstetrics and Gynaecology, Fetal Medicine Unit, Mount Sinai Hospital and University of Toronto, Toronto, Ontario, Canada.

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