Automated detection of imaging features of disproportionately enlarged subarachnoid space hydrocephalus using machine learning methods.

Journal: NeuroImage. Clinical
PMID: 30497983

Abstract

OBJECTIVE: Create an automated classifier for imaging characteristics of disproportionately enlarged subarachnoid space hydrocephalus (DESH), a neuroimaging phenotype of idiopathic normal pressure hydrocephalus (iNPH).

Authors

  • Nathaniel B Gunter
    Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA; University of Oklahoma, Norman, OK, USA.
  • Christopher G Schwarz
    Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA.
  • Jonathan Graff-Radford
    Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA.
  • Jeffrey L Gunter
    Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA. Electronic address: gunter.jeffrey@mayo.edu.
  • David T Jones
    Department of Computer Science, Bioinformatics Group, University College London, Gower Street, London, WC1E 6BT, United Kingdom. d.t.jones@ucl.ac.uk.
  • Neill R Graff-Radford
    Department of Neurology, Mayo Clinic and Foundation, Jacksonville, FL, USA.
  • Ronald C Petersen
    Department of Neurology, Mayo Clinic, Rochester, USA.
  • David S Knopman
    Department of Neurology, Mayo Clinic and Foundation, Rochester, MN, USA.
  • Clifford R Jack
    Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA.

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