Deep Learning to Optimize Magnetic Resonance Imaging Prediction of Motor Outcomes After Hypoxic-Ischemic Encephalopathy.

Journal: Pediatric neurology
PMID: 37774643

Abstract

BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for outcome prediction after hypoxic-ischemic encephalopathy (HIE). Published scoring systems contain duplicative or conflicting elements.

Authors

  • Zachary A Vesoulis
    Division of Newborn Medicine, Department of Pediatrics, Washington University, St. Louis, Missouri. Electronic address: vesoulis_z@wustl.edu.
  • Shamik B Trivedi
    Division of Neonatology, Department of Pediatrics, Northwestern University, Chicago, Illinois.
  • Hallie F Morris
    Division of Neonatology, Children's National Medical Center, Washington, District of Columbia.
  • Robert C McKinstry
    Department of Pediatrics, Washington University School of Medicine, Saint Louis, Missouri; Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri.
  • Yi Li
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Amit M Mathur
    Division of Neonatology, Department of Pediatrics, Saint Louis University, St. Louis, Missouri.
  • Yvonne W Wu
    Department of Neurology, UCSF, San Francisco, California.

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