Evidence map›Paper›PMID 37888736›Full record

ArticleTomography (Ann Arbor, Mich.)2023

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

Open access · goldAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Alameen DamerDepartment of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.ORCID 0000-0003-3953-5822
Emaan ChaudryDepartment of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.ORCID 0000-0002-5724-0027
Daniel EftekhariDepartment of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.
Susanne M BenselerDepartment of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
Frozan SafiDepartment of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.
Richard I AvivDepartment of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Pascal N TyrrellDepartment of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada.ORCID 0000-0003-2277-3824
University of Toronto · CAUniversity of Ottawa · CAUniversity of Calgary · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceVasculitisCentral Nervous SystemHumansMachine LearningNeuroimagingartificial intelligenceCNSinflammatorymachine learningneuroimagingsmall-vessel vasculitis

Identifiers

PMID37888736
PMCPMC10610796
OpenAlexW4387258954

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.