Neuroimaging Scoring Tools to Differentiate Inflammatory Central Nervous System Small-Vessel Vasculitis: A Need for Artificial Intelligence/Machine Learning?—A Scoping Review
Alameen Damer,
Emaan Chaudry,
Daniel Eftekhari,
Susanne M. Benseler,
Frozan Safi,
Richard I. Aviv,
Pascal N. Tyrrell
Affiliations
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, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Pascal N. Tyrrell
Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
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.
