Evidence map›Paper›PMID 42839215›Full record

ReviewNeurology and therapy2026

Artificial Intelligence for Gadolinium-Sparing Detection of Active Multiple Sclerosis Lesions: A Structured Narrative Review.

Elham Moases Ghaffary, Omid Mirmosayyeb, Saeedeh Mirbagheri

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In one paragraph

Review in Neurology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Elham Moases GhaffaryDivision of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, MO, USA.
Omid MirmosayyebDepartment of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, VT, USA. Omid.mirmosayyeb@uvmhealth.org.ORCID http://orcid.org/0000-0002-3756-2985
Saeedeh MirbagheriDepartment of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionGadolinium-enhanced T1-weighted magnetic resonance imaging (MRI; T1c+) remains the reference standard for identifying active inflammatory lesions in multiple sclerosis (MS). However, cumulative exposure raises concerns regarding tissue deposition, cost, scan duration, and patient burden. Artificial intelligence (AI) has emerged as a potential strategy to reduce reliance on contrast administration.

methodsWe conducted a structured narrative review of studies evaluating AI-based approaches for detecting active MS lesions without routine gadolinium use. Eligible studies included those that generated synthetic contrast-equivalent images or directly classified lesion activity using non-contrast MRI. Outcomes included diagnostic test accuracy, reconstruction fidelity, model characteristics, and methodological quality, which were assessed using an adapted QUADAS-2 framework.

resultsAmong studies reporting diagnostic accuracy against a gadolinium reference standard, sensitivity ranged from 72% to 100% and specificity from 66% to 96%. Only one study reported a complete contingency table; a second reported confusion matrices that are not reproduced here, and only one classifier included external multicenter validation. Image-synthesis studies demonstrated visually plausible outputs but were frequently validated against non-gadolinium targets, limiting clinical applicability. Common limitations included lack of prospective validation, limited reporting transparency, and insufficient evaluation of generalizability.

conclusionAI-based approaches demonstrate feasibility for gadolinium-sparing MS imaging but are not yet sufficient to replace contrast-enhanced MRI in routine clinical practice. A plausible near-term role is as a triage tool to identify low-risk follow-up examinations in which gadolinium administration may be deferred. Prospective multicenter studies with standardized reporting and true post-contrast reference standards are needed before clinical adoption.

Indexed as

Deep learningGadoliniumGenerative adversarial networkMagnetic resonance imagingMultiple sclerosis

Identifiers

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Registered trials

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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.