Evidence map›Paper›PMID 31981517›Full record

Trial reportThe lancet. Gastroenterology & hepatology2020

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

Pu Wang, Xiaogang Liu, Tyler M Berzin, Jeremy R Glissen Brown, Peixi Liu, Chao Zhou, Lei Lei, Liangping Li, Zhenzhen Guo, Shan Lei and 5 more

Erratum issued 5 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 5 registered trials, which are not on this map. Cited by 192 papers, 19 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
192citing papers in PubMed, 19 pooled it
23.2field-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.

NCT04636164 naterminatednot on this map

Effect of Using Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologist Physician

TypeinterventionalSponsorPyoeng Gyun ChoeRan2020 to 2021Enrolled55ConditionsSkin DiseasesArmsModel Dermatology (deep neural networks, Build 2020)
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 map

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
NCT04945044 nacompletednot on this mapstarted 2021, after this paper: background citation

Usefulness of the Endo-AID Artificial Intelligence System in the Detection of Colorectal Adenomas. a Randomized Controlled Trial

TypeinterventionalSponsorHospital Universitario de CanariasRan2021 to 2022Enrolled370ConditionsAdenoma Detection RateArmsComputed adenoma detection system (CADe), Control group (regular colonoscopy)
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
3 · Its place in the literature

Who cites it

192 citing papers in PubMed, 19 syntheses or guidelines pooled it, 397 citations in OpenAlex.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 2 institutions in 2 countries.

Pu WangDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Xiaogang LiuDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Tyler M BerzinCenter for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
Jeremy R Glissen BrownCenter for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
Peixi LiuDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Chao ZhouDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Lei LeiDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Liangping LiDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Zhenzhen GuoDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Shan LeiDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Fei XiongDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Han WangDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Yan SongDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Yan PanDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
Guanyu ZhouDepartment of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China. Electronic address: zhou.guanyu@outlook.com.
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

backgroundColonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system that avoids potential operational bias.

methodsWe did a double-blind randomised trial at the endoscopy centre in Caotang branch hospital of Sichuan Provincial People's Hospital in China. We enrolled consecutive patients (aged 18-75 years) presenting for diagnostic and screening colonoscopy. We excluded patients with a history of inflammatory bowel disease, colorectal cancer, or colorectal surgery or who had a contraindication for biopsy; we also excluded patients who had previously had an unsuccessful colonoscopy and who had a high suspicion for polyposis syndromes, inflammatory bowel disease, and colorectal cancer. We allocated patients (1:1) to colonoscopy with either the CADe system or a sham system. Randomisation was by computer-generated random number allocation. Patients and the endoscopist were unaware of the random assignment. To achieve masking, the output of the system was shown on a second monitor that was only visible to an observer who was responsible for reporting the alerts. The primary outcome was the adenoma detection rate (ADR), which is the proportion of individuals having a complete colonoscopy, from caecum to rectum, who had one or more adenomas detected. The primary analysis was per protocol. We also analysed characteristics of polyps and adenomas missed initially by endoscopists but detected by the CADe system. This trial is complete and is registered with http://www.chictr.org.cn, ChiCTR1800017675.

findingsBetween Sept 3, 2018, and Jan 11, 2019, 1046 patients were enrolled to the study, of whom 36 were excluded before randomisation, 508 were allocated colonoscopy with polyp detection using the CADe system, and 502 were allocated colonoscopy with the sham system. After further excluding patients who met exclusion criteria, 484 patients in the CADe group and 478 in the sham group were included in analyses. The ADR was significantly greater in the CADe group than in the sham group, with 165 (34%) of 484 patients allocated to the CADe system having one or more adenomas detected versus 132 (28%) of 478 allocated to the sham system (odds ratio 1·36, 95% CI 1·03-1·79; p=0·030). No complications were reported among all colonoscopy procedures. Polyps initially missed by the endoscopist but identified by the CADe system were generally small in size, isochromatic, flat in shape, had an unclear boundary, were partly behind colon folds, and were on the edge of the visual field.

interpretationPolyps initially missed by the endoscopist had characteristics that are sometimes difficult for skilled endoscopists to recognise. Such polyps could be detected using a high-performance CADe system during colonoscopy. The effect of CADe during colonoscopy on the incidence of interval colorectal cancer should be investigated.

fundingNone.

Indexed as

AdenomaAdultCase-Control StudiesChinaClinical AlarmsColonic PolypsColonoscopyColorectal NeoplasmsDeep LearningDiagnosis, Computer-AssistedDouble-Blind MethodEarly DiagnosisFemaleHumansIncidenceMale

Identifiers

PMID31981517
OpenAlexW3001365238

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.