Evidence map›Paper›PMID 39702564›Full record

Trial reportSurgical endoscopy2025

Optimized computer-assisted technique for increasing adenoma detection during colonoscopy: a randomized controlled trial.

Roupen Djinbachian, Mahsa Taghiakbari, Alan Barkun, Edgard Medawar, Abla Alj, Sacha Sidani, Jeremy Liu Chen Kiow, Benoit Panzini, Mickael Bouin, Dina Lasfar and 1 more

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
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

11 authors.

Roupen Djinbachian *Montreal University Hospital Research Center, Montreal, QC, Canada.
Mahsa Taghiakbari *Montreal University Hospital Research Center, Montreal, QC, Canada.
Alan BarkunDivision of Gastroenterology, McGill University Health Center (MUHC), Montreal, QC, Canada.
Edgard MedawarMontreal University Hospital Research Center, Montreal, QC, Canada.
Abla AljMontreal University Hospital Research Center, Montreal, QC, Canada.
Sacha SidaniDivision of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal, QC, Canada.
Jeremy Liu Chen KiowDivision of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal, QC, Canada.
Benoit PanziniDivision of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal, QC, Canada.
Mickael BouinDivision of Gastroenterology, Montreal University Hospital Center (CHUM), Montreal, QC, Canada.
Dina LasfarMontreal University Hospital Research Center, Montreal, QC, Canada.
Daniel von RentelnMontreal University Hospital Research Center, Montreal, QC, Canada. danielrenteln@gmail.com.

Funding

Fujifilm Corporation 21.305
6 · The paper itself

Abstract

backgroundEfforts to improve colonoscopy have recently focused on improving adenoma detection through individual interventions. We evaluated an optimized computer-assisted technique (CADopt) versus standard colonoscopy.

methodsA prospective randomized controlled trial was conducted enrolling adults (45-80 years) undergoing elective colonoscopy. Participants were randomized (1:1) to the intervention group (CADopt), and control group. In the CADopt group, endoscopists used a computer-aided polyp detection combined with linked color imaging, water exchange colonoscopy, and cecal retroflexion. In the control group, standard colonoscopy was performed. Primary outcome was Adenoma Detection Rate (ADR) in the intervention and control groups. Secondary outcomes included polyp detection rate (PDR), advanced ADR (AADR), sessile-serrated lesion detection rates (SDR), and Adenoma per colonoscopy (APC).

resultsA total of 467 patients were recruited and randomized (CADopt group 229 patients, 50.2% female vs 238 patients, 48.3% female in the control group). ADR was 49.3% (95% CI 42.7-56.0) for the CADopt group vs 38.2% (95% CI 32.0-44.7) for the control group (p = 0.016). PDR, AADR, SDR, and APC were 78.2% (95% CI 72.2-83.3), 13.1% (95% CI 9.0-18.2), 6.6% (95% CI 3.7-10.6), and 0.86 (95% CI 0.70-1.02) for the CADopt group versus 59.2% (95% CI 52.7-65.5), 8.0% (95% CI 4.9-12.2), 7.1% (95% CI 4.2-11.1), and 0.75 (95% CI 0.58-0.92) for the control group, respectively.

conclusionUsing an optimized computer-assisted technique led to significant improvements in ADR, PDR, and a trend towards AADR improvements.

Indexed as

AdenomaColonic NeoplasmsColonic PolypsColonoscopyColorectal NeoplasmsDiagnosis, Computer-AssistedAgedAged, 80 and overFemaleHumansMaleMiddle AgedProspective StudiesAdenoma detectionArtificial IntelligenceCADeCecal retroflexionColonoscopyPolyp detection

Identifiers

PMID39702564

What OpenQuestion holds

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Read underepoch 390

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.