Evidence map›Paper›PMID 31981518›Full record

Trial reportThe lancet. Gastroenterology & hepatology2020

Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study.

Dexin Gong, Lianlian Wu, Jun Zhang, Ganggang Mu, Lei Shen, Jun Liu, Zhengqiang Wang, Wei Zhou, Ping An, Xu Huang and 16 more

Erratum issued 3 registry-linked trialsAbstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in The lancet. Gastroenterology & hepatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to 3 registered trials, which are not on this map. Cited by 172 papers, 16 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
172citing papers in PubMed, 16 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.

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 map

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
NCT07395570 naactive not recruitingnot on this mapstarted 2025, after this paper: background citation

Efficacy of Computer Aided Detection (CADe) System in Detecting Gastric Neoplasia - a Prospective Tandem Study

TypeinterventionalSponsorChinese University of Hong KongRan2025 to 2028Enrolled260ConditionsGastric NeoplasiaArmsCADe System
3 · Its place in the literature

Who cites it

172 citing papers in PubMed, 16 syntheses or guidelines pooled it.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

26 authors.

Dexin GongDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Lianlian WuDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Jun ZhangDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Ganggang MuDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Lei ShenDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Jun LiuHubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
Zhengqiang WangDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Wei ZhouDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Ping AnDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Xu HuangDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Xiaoda JiangDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Yanxia LiDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Xinyue WanDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Shan HuSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, China.
Yiyun ChenSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, China.
Xiao HuSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, China.
Youming XuDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Xiaoyun ZhuKey Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
Suqin LiKey Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
Liwen YaoDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Xinqi HeDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Di ChenDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Li HuangDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Xiao WeiKey Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
Xuemei WangKey Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
Honggang YuDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China. Electronic address: yuhonggang1968@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundColonoscopy performance varies among endoscopists, impairing the discovery of colorectal cancers and precursor lesions. We aimed to construct a real-time quality improvement system (ENDOANGEL) to monitor real-time withdrawal speed and colonoscopy withdrawal time and to remind endoscopists of blind spots caused by endoscope slipping. We also aimed to evaluate the effectiveness of this system for improving adenoma yield of everyday colonoscopy.

methodsThe ENDOANGEL system was developed using deep neural networks and perceptual hash algorithms. We recruited consecutive patients aged 18-75 years from Renmin Hospital of Wuhan University in China who provided written informed consent. We randomly assigned patients (1:1) using computer-generated random numbers and block randomisation (block size of four) to either colonoscopy with the ENDOANGEL system or unassisted colonoscopy (control). Endoscopists were not masked to the random assignment but analysts and patients were unaware of random assignments. The primary endpoint was the adenoma detection rate (ADR), which is the proportion of patients having one or more adenomas detected at colonoscopy. The primary analysis was done per protocol (ie, in all patients having colonoscopy done in accordance with the assigned intervention) and by intention to treat (ie, in all randomised patients). This trial is registered with http://www.chictr.org.cn, ChiCTR1900021984.

findingsBetween June 18, 2019, and Sept 6, 2019, 704 patients were randomly allocated colonoscopy with the ENDOANGEL system (n=355) or unassisted (control) colonoscopy (n=349). In the intention-to-treat population, ADR was significantly greater in the ENDOANGEL group than in the control group, with 58 (16%) of 355 patients allocated ENDOANGEL-assisted colonoscopy having one or more adenomas detected, compared with 27 (8%) of 349 allocated control colonoscopy (odds ratio [OR] 2·30, 95% CI 1·40-3·77; p=0·0010). In the per-protocol analysis, findings were similar, with 54 (17%) of 324 patients assigned ENDOANGEL-assisted colonoscopy and 26 (8%) of 318 patients assigned control colonoscopy having one or more adenomas detected (OR 2·18, 95% CI 1·31-3·62; p=0·0026). No adverse events were reported.

interpretationThe ENDOANGEL system significantly improved the adenoma yield during colonoscopy and seems to be effective and safe for use during routine colonoscopy.

fundingHubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Hubei Province Major Science and Technology Innovation Project, and the National Natural Science Foundation of China.

Indexed as

AdenomaAdultAlgorithmsCase-Control StudiesChinaColonic PolypsColonoscopyDiagnosis, Computer-AssistedEarly DiagnosisFemaleHumansIntention to Treat AnalysisMaleMiddle AgedNeural Networks, ComputerOptic Disk

Identifiers

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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.