Evidence map›Paper›PMID 30814121›Full record

Trial reportGut2019

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.

Pu Wang, Tyler M Berzin, Jeremy Romek Glissen Brown, Shishira Bharadwaj, Aymeric Becq, Xun Xiao, Peixi Liu, Liangping Li, Yan Song, Di Zhang and 4 more

7 registry-linked trialsOpen access · hybridAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Gut, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 7 registered trials, which are not on this map. Cited by 382 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
382citing papers in PubMed, 14 pooled it
45.4field-weighted citation impact, top 1% 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.

NCT04196088 naunknown statusnot on this mapstarted 2020, after this paper: background citation

AIDA: A Multicenter, Prospective, Randomized Controlled Trial to Evaluate the Clinical Efficacy and Safety of Artificial Intelligence (AI) Detection of Adenomas (AIDA) With Standard High Definition With Light in Screening Colonoscopy

TypeinterventionalSponsorDocbot, Inc.Ran2020 to 2021Enrolled978ConditionsColorectal Adenoma, Colorectal Polyp, Colorectal Adenocarcinoma, Colorectal SSAArmsUltivision Artificial Intelligence Software, No Artificial Intelligence Software enhancement
NCT04555135 naterminatednot on this mapstarted 2020, after this paper: background citation

A Prospective, Multi-Center, Randomized Controlled Clinical Study To Measure The Effect Of Use Of Artificial Intelligence (AI) Enabled Computer Aided Detection (CADe) Assistance Software In Detecting Colon Polyps During Standard Colonoscopy Procedures

TypeinterventionalSponsorEndoVigilant IncRan2020 to 2021Enrolled769ConditionsColon Polyp, Colon Neoplasm, Colon AdenomaArmsEndoVigilant Software
NCT04673136 nacompletednot on this mapstarted 2021, after this paper: background citation

Usefulness of GI-GENIUS in FIT-based Colorectal Cancer Screening Program. Randomized Controlled Trial.

TypeinterventionalSponsorAsociación Española de GastroenterologíaRan2021 to 2022Enrolled3,400ConditionsColorectal Neoplasms, Intestinal Neoplasms, Gastrointestinal Neoplasms, Colonic PolypArmsGI-GENIUS Medtronic, Colonoscopy
NCT04727814 unknown statusnot on this mapstarted 2020, after this paper: background citation

Polyp Detection and False Alarm Rates by Computer-Aided Analysis of Videos of Withdrawal Phase of Colonoscopy in a Randomized Controlled Trial of Water Exchange Versus Air Insufflation

TypeobservationalSponsorDalin Tzu Chi General HospitalRan2020 to 2021Enrolled250ConditionsColon PolypArmsComputer-aided detection system overlaid colonoscopy videos analysis
NCT04915833 naunknown statusnot on this mapstarted 2021, after this paper: background citation

Real-time Computer-aided Polyp Detection During Screening Colonoscopy Performed by Expert Endoscopists

TypeinterventionalSponsorInstituto Ecuatoriano de Enfermedades DigestivasRan2021 to 2022Enrolled209ConditionsColorectal Neoplasms, Colon Polyp, Colon AdenomaArmsStandard high-definition colonoscopy, Colonoscopy with real-time AI assisted automated polyp detection
NCT05034185 completednot on this mapstarted 2021, after this paper: background citation

Real-World Validation of an Artificial Intelligence Characterization Support (CADx) System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicentre Study

TypeobservationalSponsorChangi General HospitalRan2021 to 2022Enrolled450ConditionsColonic Polyp, Colonic Neoplasms, Colonic DysplasiaArmsComputer-aided diagnosis (CADx) support tool
NCT05734820 naunknown statusnot on this mapstarted 2020, after this paper: background citation

Real-time Computer-aided Polyp/Adenoma Detection During Screening Colonoscopy: a Single-center Crossover Trial

TypeinterventionalSponsorInstituto Ecuatoriano de Enfermedades DigestivasRan2020 to 2024Enrolled312ConditionsColorectal Polyp, Colorectal Cancer, Colorectal AdenomaArmsHD- colonoscopy, HD-colonoscopy assisted by AI
3 · Its place in the literature

Who cites it

382 citing papers in PubMed, 14 syntheses or guidelines pooled it, 801 citations in OpenAlex.

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322 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 3 countries.

Pu WangDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.ORCID 0000-0002-1234-309X
Tyler M BerzinCenter for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-4364-6210
Jeremy Romek Glissen BrownCenter for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-7204-7241
Shishira BharadwajCenter for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.
Aymeric BecqCenter for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.
Xun XiaoDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Peixi LiuDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Liangping LiDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Yan SongDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Di ZhangDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Yi LiDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Guangre XuDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Mengtian TuDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Xiaogang LiuDepartment of Gastroenterology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.ORCID 0000-0001-9186-6720
Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital · CNBeth Israel Deaconess Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR.

designIn an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR.

resultsOf 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001). This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001).

conclusionsIn a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost-benefit ratio of such effects has to be determined further. TRIAL REGISTRATION NUMBER: ChiCTR-DDD-17012221; Results.

Indexed as

AdenomaChinaColonic PolypsColonoscopyColorectal NeoplasmsEarly Detection of CancerFemaleFollow-Up StudiesHumansIncidenceMaleMiddle AgedProspective StudiesReproducibility of ResultsTime Factorscolonoscopycolorectal cancer screeningcomputerised image analysis

Identifiers

PMID30814121
PMCPMC6839720
OpenAlexW2916105049

What OpenQuestion holds

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LicenceCC BY-NC
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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.