Fast and robust unsupervised identification of MS lesion change using the statistical detection of changes algorithm Academic Article uri icon

Overview

MeSH Major

  • Imaging, Three-Dimensional
  • Iron
  • Parkinson Disease
  • Substantia Nigra

abstract

  • © 2018 American Society of Neuroradiology. All rights reserved. We developed a robust automated algorithm called statistical detection of changes for detecting morphologic changes of multiple sclerosis lesions between 2 T2-weighted FLAIR brain images. Results from 30 patients showed that statistical detection of changes achieved significantly higher sensitivity and specificity (0.964, 95% CI, 0.823- 0.994; 0.691, 95% CI, 0.612- 0.761) than with the lesionprediction algorithm (0.614, 95% CI, 0.410-0.784; 0.281, 95% CI, 0.228-0.314), while resulting in a 49% reduction in human review time (P = .007).

publication date

  • May 2018

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC5955764

Digital Object Identifier (DOI)

  • 10.3174/ajnr.A5594

PubMed ID

  • 29519791

Additional Document Info

start page

  • 830

end page

  • 833

volume

  • 39

number

  • 5