Neuroimaging Scoring Tools to Differentiate Inflammatory Central Nervous System Small-Vessel Vasculitis: A Need for Artificial Intelligence/Machine Learning?-A Scoping Review.

Journal: Tomography (Ann Arbor, Mich.)
PMID:

Abstract

Neuroimaging has a key role in identifying small-vessel vasculitis from common diseases it mimics, such as multiple sclerosis. Oftentimes, a multitude of these conditions present similarly, and thus diagnosis is difficult. To date, there is no standardized method to differentiate between these diseases. This review identifies and presents existing scoring tools that could serve as a starting point for integrating artificial intelligence/machine learning (AI/ML) into the clinical decision-making process for these rare diseases. A scoping literature review of EMBASE and MEDLINE included 114 articles to evaluate what criteria exist to diagnose small-vessel vasculitis and common mimics. This paper presents the existing criteria of small-vessel vasculitis conditions and mimics them to guide the future integration of AI/ML algorithms to aid in diagnosing these conditions, which present similarly and non-specifically.

Authors

  • Alameen Damer
    Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.
  • Emaan Chaudry
    Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.
  • Daniel Eftekhari
    Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.
  • Susanne M Benseler
    Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
  • Frozan Safi
    Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.
  • Richard I Aviv
    Department of Radiology and Medical Imaging, University of Ottawa, Ottawa, Canada.
  • Pascal N Tyrrell
    Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada; Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada. Electronic address: pascal.tyrrell@utoronto.ca.