Rule-based deep learning method for prognosis of neonatal hypoxic-ischemic encephalopathy by using susceptibility weighted image analysis.

Journal: Magma (New York, N.Y.)
PMID: 38252196

Abstract

OBJECTIVE: Susceptibility weighted imaging (SWI) of neonatal hypoxic-ischemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE.

Authors

  • Zhen Tang
    Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA.
  • Sasan Mahmoodi
    School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.
  • Di Meng
    School of Computer Science and Technology, AnHui University of Technology, Maxiang Street, Maanshan, 243032, Anhui, China.
  • Angela Darekar
    Department of Medical Physics, University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK.
  • Brigitte Vollmer
    Clinical Neurosciences and Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK.

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