Evidence map›Paper›PMID 42816641›Full record

ArticleSurgical endoscopy2026

Bowel preparation and adenoma detection in AI-assisted colonoscopy.

Giacomo Mulinacci, Roberto Frego, Marta Maino, Alessandro Ettore Redaelli, Ilaria Ripamonti, Marco Emilio Dinelli, Clara Benedetta Conti

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In one paragraph

Article 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.

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1 · What the graph read from it

What it found

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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

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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.

Giacomo MulinacciInterventional Endoscopy Unit, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 3, Monza, Italy. giacomo.mulinacci@irccs-sangerardo.it.
Roberto FregoInterventional Endoscopy Unit, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 3, Monza, Italy.
Marta MainoInterventional Endoscopy Unit, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 3, Monza, Italy.
Alessandro Ettore RedaelliInterventional Endoscopy Unit, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 3, Monza, Italy.
Ilaria RipamontiInterventional Endoscopy Unit, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 3, Monza, Italy.
Marco Emilio DinelliInterventional Endoscopy Unit, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 3, Monza, Italy.
Clara Benedetta ContiInterventional Endoscopy Unit, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 3, Monza, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimArtificial intelligence (AI)-assisted colonoscopy improves adenoma detection rate (ADR), but it is currently unknown whether it performs better with excellent vs good bowel cleansing. This study aims to evaluate the association between bowel cleansing with ADR and adenomas per colonoscopy (APC) in average-risk patients undergoing AI-assisted colonoscopy.

methodsProspective single-centre study. Consecutive average-risk patients undergoing AI-assisted colonoscopy were enrolled from December 2025 to March 2026. Demographic and endoscopic variables were collected. Bowel preparation quality was assessed using the Boston Bowel Preparation Scale (BBPS); adequate cleansing was defined as BBPS ≥ 6 and classified as good (6-7) or excellent (≥ 8). Univariable and multivariable regression analyses identified variables associated with ADR and APC.

resultsA total of 425 patients were included. The overall ADR and APC were 0.35 and 0.6, respectively. Bowel preparation quality was not associated with ADR or APC. On multivariable analysis, ADR was associated with: BMI (OR 1.05, p = 0.041), female sex (OR 0.39, p < 0.001) and afternoon examinations (OR 0.39, p = 0.013). Female sex (IRR 0.53, p < 0.001) and higher BMI (IRR 1.05, p = 0.031) were independently associated with APC.

conclusionsExcellent bowel preparation quality was not associated with higher ADR or APC compared with good bowel preparation in this cohort. Female sex, BMI, and timing of colonoscopy were independently associated with ADR, despite the use of computer-aided detection systems.

Indexed as

Adenoma detection rateArtificial intelligenceBowel preparationColonoscopyComputer-aided detection

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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.