Evidence map›Paper›PMID 38290758›Full record

Trial reportBMJ open gastroenterology2024

Impact of AI-aided colonoscopy in clinical practice: a prospective randomised controlled trial.

Johanna Schöler, Marko Alavanja, Thomas de Lange, Shunsuke Yamamoto, Per Hedenström, Jonas Varkey

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in BMJ open gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05178095 (Artificial Intelligence in Colonic Polyp Detection), which is not on this map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
6.5field-weighted citation impact, top 2% of its field
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.

NCT05178095 nacompletednot on this map

Artificial Intelligence in Colonic Polyp Detection

TypeinterventionalSponsorSahlgrenska University HospitalRan2020 to 2023Enrolled250ConditionsPolyp of Colon, Adenoma Colon, Gastrointestinal NeoplasmsArmsArtificial intelligence, No Artificial Intelligence
3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 23 citations in OpenAlex.

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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

6 authors at 1 institution in 2 countries.

Johanna SchölerMedical Department, Sahlgrenska University Hospital, Goteborg, Sweden.
Marko AlavanjaDepartment of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Goteborg, Sweden.
Thomas de LangeMedical Department, Sahlgrenska University Hospital, Goteborg, Sweden.
Shunsuke YamamotoDepartment of Medicine, Sahlgrenska University Hospital, Goteborg, Sweden.
Per HedenströmMedical Department, Sahlgrenska University Hospital, Goteborg, Sweden.
Jonas VarkeyDepartment of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Goteborg, Sweden jonas.varkey@gu.se.ORCID 0000-0002-3499-7051
Sahlgrenska University Hospital · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveColorectal cancer (CRC) has a significant role in cancer-related mortality. Colonoscopy, combined with adenoma removal, has proven effective in reducing CRC incidence. However, suboptimal colonoscopy quality often leads to missed polyps. The impact of artificial intelligence (AI) on adenoma and polyp detection rate (ADR, PDR) is yet to be established.

designWe conducted a randomised controlled trial at Sahlgrenska University Hospital in Sweden. Patients underwent colonoscopy with or without the assistance of AI (AI-C or conventional colonoscopy (CC)). Examinations were performed with two different AI systems, that is, Fujifilm CADEye and Medtronic GI Genius. The primary outcome was ADR.

resultsAmong 286 patients, 240 underwent analysis (average age: 66 years). The ADR was 42% for all patients, and no significant difference emerged between AI-C and CC groups (41% vs 43%). The overall PDR was 61%, with a trend towards higher PDR in the AI-C group. Subgroup analysis revealed higher detection rates for sessile serrated lesions (SSL) with AI assistance (AI-C 22%, CC 11%, p=0.004). No difference was noticed in the detection of polyps or adenomas per colonoscopy. Examinations were most often performed by experienced endoscopists, 78% (n=86 AI-C, 100 CC).

conclusionAmidst the ongoing AI integration, ADR did not improve with AI. Particularly noteworthy is the enhanced detection rates for SSL by AI assistance, especially since they pose a risk for postcolonoscopy CRC. The integration of AI into standard colonoscopy practice warrants further investigation and the development of improved software might be necessary before enforcing its mandatory implementation. TRIAL REGISTRATION NUMBER: NCT05178095.

Indexed as

AdenomaArtificial IntelligenceAgedColonoscopyEarly Detection of CancerHumansProspective StudiesCOLONIC ADENOMASCOLONIC POLYPSCOLONOSCOPYCOLORECTAL CANCER

Identifiers

PMID38290758
PMCPMC10870789
OpenAlexW4391345093

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.