Evidence map›Paper›PMID 42443688›Full record

SynthesisSurgical endoscopy2026

Artificial intelligence with mucosal exposure devices versus artificial intelligence alone in detection of colorectal adenomas: a systematic review and meta-analysis.

Aamir Saeed, Saira Yousuf, Ijlal Akbar Ali, Mark Radlinski, Faisal Kamal, Claudio R Tombazzi, Leonard Baidoo, Mansour A Parsi, Tyler M Berzin, Sultan Mahmood

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Surgical endoscopy, 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.

Aamir SaeedDivision of Gastroenterology and Hepatology, University of Tennessee Health Sciences Center, 920 Madison Avenue, Memphis, TN, 38163, USA. aamirsaeed4444@gmail.com.ORCID http://orcid.org/0000-0001-8752-4867
Saira YousufDepartment of Medicine, University of Tennessee Health Sciences Center, Memphis, TN, USA.
Ijlal Akbar AliDivision of Gastroenterology and Hepatology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Mark RadlinskiDivision of Gastroenterology and Hepatology, Vanderbilt University Medical Center, Nashville, TN, USA.
Faisal KamalDivision of Gastroenterology and Hepatology, Thomas Jefferson University Hospital, Philadelphia, PA, USA.
Claudio R TombazziDivision of Gastroenterology and Hepatology, University of Tennessee Health Sciences Center, 920 Madison Avenue, Memphis, TN, 38163, USA.
Leonard BaidooDivision of Gastroenterology and Hepatology, University of Tennessee Health Sciences Center, 920 Madison Avenue, Memphis, TN, 38163, USA.
Mansour A ParsiDivision of Gastroenterology and Hepatology, University of Tennessee Health Sciences Center, 920 Madison Avenue, Memphis, TN, 38163, USA.
Tyler M BerzinDivision of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Sultan MahmoodDivision of Gastroenterology and Hepatology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsBoth artificial intelligence (AI) and mucosal exposure devices (MEDs) have been shown to improve adenoma detection rate and reduce miss rate during colonoscopy. However, the effectiveness of AI combined with MEDs remains unclear. In this meta-analysis, we have evaluated the combined effect of AI with MEDs on enhancing detection rates of various colonic lesions.

methodsSeveral databases were reviewed to identify studies reporting data on outcomes of interest from inception to December 10, 2025. Our outcomes of interest were the adenoma detection rate (ADR), adenomas per colonoscopy (APC), sessile serrated detection rate (SSDR), and advanced adenoma detection rate (aADR). Further subgroup analyses were performed based on adenoma size, location, and mucosal exposure device type. Data were analyzed using the Mantel-Haenszel random-effects model. Heterogeneity was assessed using the I

resultsTwelve studies (8 RCTs, 2 retrospective, and 2 prospective) with a total of 6155 patients (2700 AI + MED, 3455 AI alone group) met the inclusion criteria. AI + MED were associated with higher rates of ADR, RR 1.15 (95% CI 1.07-1.25; p = 0.0005) as compared to AI alone, and subgroup analysis on device type also showed higher rates for AI + Endocuff RR 1.19 (95% CI 1.08-1.32; p = 0.0006) and AI + balloon device did not show significant results for ADR, RR 1.08 (95% CI 0.93-1.27; p = 0.32) between the groups, however, the formal test for subgroup differences between device type was not statistically significant (P = 0.33). There was no significant difference in aADR, SSDR, proximal ADR, cecal intubation time, withdrawal time, ADRs for size < 5 mm, and ADRs for size 6-9 mm between AI + MED vs. AI alone.

conclusionsAI-assisted colonoscopy augmented with a mucosal exposure device, particularly Endocuff, significantly enhanced ADR compared with AI alone, while aADR, proximal ADR overall, SSDR, and procedure times were comparable between strategies. These findings suggest that the selective use of Endocuff in AI-enabled colonoscopy may optimize lesion detection.

Indexed as

AdenomaArtificial IntelligenceColonoscopyColorectal NeoplasmsHumansIntestinal MucosaAdenoma detection rateArtificial intelligenceColonoscopyMucosal exposure devices

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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.