Automated Neuroprognostication Via Machine Learning in Neonates with Hypoxic-Ischemic Encephalopathy.

Journal: Annals of neurology
PMID: 39655476

Abstract

OBJECTIVES: Neonatal hypoxic-ischemic encephalopathy is a serious neurologic condition associated with death or neurodevelopmental impairments. Magnetic resonance imaging (MRI) is routinely used for neuroprognostication, but there is substantial subjectivity and uncertainty about neurodevelopmental outcome prediction. We sought to develop an objective and automated approach for the analysis of newborn brain MRI to improve the accuracy of prognostication.

Authors

  • John D Lewis
    McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
  • Atiyeh A Miran
    Division of Neonatology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
  • Michelle Stoopler
    Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
  • Helen M Branson
    Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
  • Ashley Danguecan
    Division of Neonatology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
  • Krishna Raghu
    Division of Neonatology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
  • Linh G Ly
    Division of Neonatology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
  • Mehmet N Cizmeci
    Division of Neonatology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Canada.
  • Brian T Kalish
    Program in Neuroscience and Mental Health, SickKids Research Institute, Toronto, Canada.

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