Evidence map›Paper›PMID 32968933›Full record

Trial reportJournal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract2021

Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study.

Yuchen Luo, Yi Zhang, Ming Liu, Yihong Lai, Panpan Liu, Zhen Wang, Tongyin Xing, Ying Huang, Yue Li, Aiming Li and 4 more

Registry-linked trialOpen access · hybridAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07338643 (The Auxiliary Effect of Artificial Intelligence in the Detection of Precancerous Lesions in Proximal Colon Cancer), which is not on this map. Cited by 45 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 9 pooled it
4.5field-weighted citation impact, top 4% 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.

NCT07338643 not yet recruitingnot on this mapstarted 2026, after this paper: background citation

The Auxiliary Effect of Artificial Intelligence in the Detection of Precancerous Lesions in Proximal Colon Cancer: A Multicenter Randomized Controlled Study

TypeobservationalSponsorLimian ErRan2026 to 2028Enrolled1,200ConditionsAbdominal Pain, Black Stools, Constipation, DiarrheaArmsAI group, Serial group, Standard colonoscopy group
3 · Its place in the literature

Who cites it

45 citing papers in PubMed, 9 syntheses or guidelines pooled it, 93 citations in OpenAlex.

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  19. [Role of Artificial Intelligence in Improving Quality of Colonoscopy].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025
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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

14 authors at 2 institutions in 1 country.

Yuchen LuoDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Yi ZhangDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Ming LiuDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Yihong LaiDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Panpan LiuDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Zhen WangDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Tongyin XingDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Ying HuangDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Yue LiDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Aiming LiDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Yadong WangDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Xiaobei LuoDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China. luoxiaobei63@126.com.
Side LiuDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China. liuside2011@163.com.
Zelong HanDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China. hzl198886@163.com.
Nanfang Hospital · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsImproving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment.

methodsThe selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov . (NCT047126265).

resultsIn this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions.

conclusionsA real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion.

trial registrationclinicaltrials.gov Identifier: NCT047126265.

Indexed as

Colonic PolypsColorectal NeoplasmsArtificial IntelligenceCohort StudiesColonColonoscopyFollow-Up StudiesHumansProspective StudiesArtificial intelligenceColonoscopyComputer-aided diagnose

Identifiers

PMID32968933
PMCPMC8321985
OpenAlexW3089088649

What OpenQuestion holds

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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.