Evidence map›Paper›PMID 42394872›Full record

ArticleiGIE : innovation, investigation and insights2026

Artificial intelligence detection of endoscopic moderate-to-severe ulcerative colitis: a novel tool to enhance clinical trial recruitment.

Laurie B Grossberg, Grace Geeganage, Aditya Mithal, Tina Deyhim, Andy Santisteban-Silva, Konstantinos Papamichael, Sami Elamin, Adam S Cheifetz, Tyler M Berzin, Loren G Rabinowitz

Abstract read
In one paragraph

Article in iGIE : innovation, investigation and insights, 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

10 authors.

Laurie B GrossbergDivision of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Grace GeeganageDivision of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Aditya MithalInternal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
Tina DeyhimDivision of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Andy Santisteban-SilvaInternal Medicine, University Health, University of Nevada Reno School of Medicine, Reno, Nevada, USA.
Konstantinos PapamichaelDivision of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Sami ElaminDivision of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Adam S CheifetzDivision of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Tyler M BerzinDivision of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Loren G RabinowitzDivision of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Artificial intelligence (AI) may detect endoscopically active ulcerative colitis (UC) and streamline clinical trial enrollment. Here, we investigate the performance of a commercially available AI model, autoinflammatory bowel disease (AutoIBD)-UC (Virgo Surgical Video Solutions, Inc, San Francisco, Calif, USA), to detect moderate-to-severe UC in colonoscopy videos collected as part of routine clinical practice. Methods: AutoIBD-UC was applied to consecutive endoscopy videos between August 30, 2024, and October 17, 2024. Each video received an AutoIBD score (0-1); videos with scores above a predefined confidence threshold were flagged as possible moderate-to-severe UC. Flagged videos and medical records were reviewed to confirm UC diagnosis and grade Mayo endoscopic subscore (MES). Concurrently, research staff retrospectively reviewed all endoscopy reports to identify 3 cohorts: (1) 20 MES ≥2, (2) 20 non-UC inflammation, and (3) 20 screening colonoscopies. Sensitivity and specificity were calculated for all videos screened by AI. Median AutoIBD scores were compared by MES and disease extent. Results: AutoIBD-UC was applied to 2273 endoscopy videos. Twenty-one videos were flagged, 10 of which had MES ≥2. AutoIBD-UC flagged 9 of 20 videos in cohort 1 and all MES 3 videos. More procedures with extensive (4 of 8) or left-sided disease (5 of 7) were flagged compared with those with proctitis (0 of 5). Median AutoIBD-UC scores differed between MES 2 (0.437; interquartile range [IQR], 0.270-0.571) and MES 3 (0.690; IQR, 0.607-0.765; Conclusions: AutoIBD-UC accurately detects moderate-to-severe UC with moderate sensitivity and high specificity. Larger studies are necessary to evaluate AutoIBD-UC's utility as a recruitment aid compared to standard practice.

Identifiers

PMID42394872
PMCPMC13324099

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LicenceCC BY-NC-ND
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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.