Evidence map›Paper›PMID 34218329›Full record

Trial reportJournal of gastroenterology2021

Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial.

Shunsuke Kamba, Naoto Tamai, Iduru Saitoh, Hiroaki Matsui, Hideka Horiuchi, Masakuni Kobayashi, Taku Sakamoto, Mai Ego, Akihiro Fukuda, Aya Tonouchi and 5 more

Registry-linked trialAbstract readMulticenter StudyRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Journal of gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05990218 (Efficacy of Artificial Intelligence in the Detection of Right Sided Colonic Polyp in Operators with Different Endoscopic Experience), which is not on this map. Cited by 51 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 10 pooled it
7.4field-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.

NCT05990218 narecruitingnot on this mapstarted 2023, after this paper: background citation

Efficacy of Artificial Intelligence in the Detection of Right Sided Colonic Polyp in Operators with Different Endoscopic Experience: a Randomized Control Trial

TypeinterventionalSponsorDepartment of Medical Services Ministry of Public Health of ThailandRan2023 to 2026Enrolled240ConditionsColon PolypArmsArtificial intellegence. CADe syste,, control
3 · Its place in the literature

Who cites it

51 citing papers in PubMed, 10 syntheses or guidelines pooled it, 115 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

15 authors at 1 institution in 1 country.

Shunsuke KambaDepartment of Endoscopy, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8461, Japan. kanba@jikei.ac.jp.ORCID 0000-0002-8491-7224
Naoto TamaiDepartment of Endoscopy, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8461, Japan.
Iduru SaitohDepartment of Endoscopy, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8461, Japan.
Hiroaki MatsuiDepartment of Endoscopy, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8461, Japan.
Hideka HoriuchiDepartment of Endoscopy, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8461, Japan.
Masakuni KobayashiDepartment of Endoscopy, The Jikei University School of Medicine Third Hospital, 4-11-1 Izumihoncho, Komae-shi, Tokyo, 201-8601, Japan.
Taku SakamotoEndoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Mai EgoEndoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Akihiro FukudaLPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
Aya TonouchiLPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
Yuki ShimaharaLPIXEL Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
Masako NishikawaClinical Research Support Center, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8461, Japan.
Haruo NishinoColoproctology Center, Matsushima Hospital, 3-138 Isecho, Nishi-ku, Yokohama-shi, Kanagawa, 220-0045, Japan.
Yutaka SaitoEndoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Kazuki SumiyamaDepartment of Endoscopy, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8461, Japan.
Jikei University School of Medicine · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural network for assisting endoscopists in detecting colorectal lesions during colonoscopy. The aim of this study was to clarify whether adenoma miss rate (AMR) could be reduced with CADe assistance during screening and surveillance colonoscopy.

methodsThis study was a multicenter randomized controlled trial. Patients aged 40 to 80 years who were referred for colorectal screening or surveillance at four sites in Japan were randomly assigned at a 1:1 ratio to either the "standard colonoscopy (SC)-first group" or the "CADe-first group" to undergo a back-to-back tandem procedure. Tandem colonoscopies were performed on the same day for each participant by the same endoscopist in a preassigned order. All polyps detected in each pass were histopathologically diagnosed after biopsy or resection.

resultsA total of 358 patients were enrolled and 179 patients were assigned to the SC-first group or CADe-first group. The AMR of the CADe-first group was significantly lower than that of the SC-first group (13.8% vs. 36.7%, P < 0.0001). Similar results were observed for the polyp miss rate (14.2% vs. 40.6%, P < 0.0001) and sessile serrated lesion miss rate (13.0% vs. 38.5%, P = 0.03). The adenoma detection rate of CADe-assisted colonoscopy was 64.5%, which was significantly higher than that of standard colonoscopy (53.6%; P = 0.036).

conclusionOur study results first showed a reduction in the AMR when assisting with CADe based on deep learning in a multicenter randomized controlled trial.

Indexed as

AdenomaAdultAgedAged, 80 and overArtificial IntelligenceColonoscopyEarly Detection of CancerFemaleHumansJapanMaleMiddle AgedRobotic Surgical ProceduresAdenoma detection rateAdenoma miss rateColonoscopyComputer-aided detectionDeep learning

Identifiers

PMID34218329
OpenAlexW3178851191

What OpenQuestion holds

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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.