The diagnostic performance of AI-based algorithms to discriminate between NMOSD and MS using MRI features: A systematic review and meta-analysis.

Journal: Multiple sclerosis and related disorders
PMID: 38781885

Abstract

BACKGROUND: Magnetic resonance imaging [MRI] findings in Neuromyelitis optica spectrum disorder [NMOSD] and Multiple Sclerosis [MS] patients could lead us to discriminate toward them. For instance, U-fiber and Dawson's finger-type lesions are suggestive of MS, however linear ependymal lesions raise the possibility of NMOSD. Recently, artificial intelligence [AI] models have been used to discriminate between NMOSD and MS based on MRI features. In this study, we aim to systematically review the capability of AI algorithms in NMOSD and MS discrimination based on MRI features.

Authors

  • Masoud Etemadifar
    School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Mahdi Norouzi
    School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. Electronic address: mdnrz1379@gmail.com.
  • Seyyed-Ali Alaei
    School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Raheleh Karimi
    Department of Epidemiology and Biostatistics, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Mehri Salari
    Functional Neurosurgery Research Center, Shohada Tajrish Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

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