Evidence map›Paper›PMID 42270324›Full record

ArticleGut and liver2026

A Scoping Review of Endoscopic Assessment Considering Disease Extent in Ulcerative Colitis: Insights for the Artificial Intelligence Era.

Yasuharu Maeda, Shin-Ei Kudo, Takanori Kuroki, Yurie Kawabata, Katsuro Ichimasa, Masashi Misawa, Noriyuki Ogata, Kazuo Ohtsuka

Abstract readScoping Review
In one paragraph

Article in Gut and liver, 2026. 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
–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

1 citing paper in PubMed.

  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

8 authors.

Yasuharu MaedaDigestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-4820-5959
Shin-Ei KudoDigestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-4268-1217
Takanori KurokiDigestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0009-0000-2080-3298
Yurie KawabataDigestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0009-0006-9078-3994
Katsuro IchimasaDigestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0001-6675-1219
Masashi MisawaDigestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-8520-2036
Noriyuki OgataDigestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0001-6407-9422
Kazuo OhtsukaDigestive Disease Center, Showa Medical University Northern Yokohama Hospital, Yokohama, Japan.ORCID 0000-0002-2409-9709

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional endoscopic indices for ulcerative colitis (UC) primarily assess the most severely affected segment, potentially underestimating the cumulative inflammatory burden across the colon. Consequently, there is increasing interest in assessment methods that incorporate both disease severity and spatial extent, including emerging artificial intelligence (AI)-based approaches. The aim of this study was to map and critically evaluate current evidence on endoscopic indices that integrate disease extent in UC, including both conventional and AI-assisted approaches. Original studies in which patients with UC were evaluated via endoscopic approaches for disease activity, severity, or extent and clinically relevant outcomes were reported. Non-original articles and studies of other diseases were excluded. PubMed/MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 2000 and December 2025. Two reviewers independently screened studies and extracted data. Findings were synthesized narratively due to heterogeneity. Twenty-one studies were included: eight addressed index development or validation, 11 evaluated clinical outcomes, and four investigated AI-assisted assessments, with some studies contributing to multiple categories. Extent-based indices showed stronger correlations with biomarkers and histological activity than conventional focal scores, although their ability to predict relapse was inconsistent. AI-based systems enabled automated assessment of inflammatory distribution, improving reproducibility and showing promising clinical associations, but external validation remains limited. Extent-integrated endoscopic assessment provides a more comprehensive evaluation of disease burden in UC patients and may improve risk stratification and treatment monitoring. However, methodological heterogeneity and limited prospective validation hinder clinical implementation. Further standardization and multicenter studies are needed.

Indexed as

Artificial IntelligenceColitis, UlcerativeColonoscopyHumansReproducibility of ResultsSeverity of Illness IndexArtificial intelligenceClinical relapseEndoscopyMucosal healing

Identifiers

PMID42270324
PMCPMC13364694

What OpenQuestion holds

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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.