Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model.

Journal: NeuroImage
PMID: 32278897

Abstract

BACKGROUND AND AIMS: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model.

Authors

  • Susmita Saha
    IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia.
  • Alex Pagnozzi
    Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
  • Pierrick Bourgeat
    CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD, Australia. Electronic address: pierrick.bourgeat@csiro.au.
  • Joanne M George
    Queensland Cerebral Palsy and Rehabilitation Research Centre, Centre for Children's Health Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • DanaKai Bradford
    Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
  • Paul B Colditz
    Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • Roslyn N Boyd
    Queensland Cerebral Palsy and Rehabilitation Research Centre, Centre for Children's Health Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • Stephen E Rose
    CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
  • Jurgen Fripp
    CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia.
  • Kerstin Pannek
    Department of Computing, Imperial College London, London, United Kingdom. Electronic address: kerstin.pannek@gmail.com.

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