Evidence map›Paper›PMID 34477306›Full record

Trial reportCancer medicine2021

Artificial intelligence-assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection.

Lei Xu, Xinjue He, Jianbo Zhou, Jie Zhang, Xinli Mao, Guoliang Ye, Qiang Chen, Feng Xu, Jianzhong Sang, Jun Wang and 3 more

Abstract readMulticenter StudyRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 4 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. [Role of Artificial Intelligence in Improving Quality of Colonoscopy].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025
    Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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

13 authors.

Lei XuDepartment of Gastroenterology, Ningbo Hospital of Zhejiang University, Ningbo, China.
Xinjue HeDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Jianbo ZhouDepartment of Gastroenterology, Yuyao People's Hospital of Zhejiang Province, Yuyao, China.
Jie ZhangDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Xinli MaoDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province, Linhai, China.
Guoliang YeDepartment of Gastroenterology, The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China.
Qiang ChenDepartment of Gastroenterology, Sanmen People's Hospital, Taizhou, China.
Feng XuDepartment of Gastroenterology, Ningbo Yinzhou People's Hospital, Ningbo, China.
Jianzhong SangDepartment of Gastroenterology, Yuyao People's Hospital of Zhejiang Province, Yuyao, China.
Jun WangDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province, Linhai, China.
Yong DingDepartment of Gastroenterology, The Affiliated Hospital of Medical School of Ningbo University, Ningbo, China.
Youming LiDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Chaohui YuDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.ORCID 0000-0003-4842-3646

Funding

Zhejiang Provincial Health Commission 2017KY581Zhejiang Provincial Health Commission 2020KY125
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) assistance has been considered as a promising way to improve colonoscopic polyp detection, but there are limited prospective studies on real-time use of AI systems.

methodsWe conducted a prospective, multicenter, randomized controlled trial of patients undergoing colonoscopy at six centers. Eligible patients were randomly assigned to conventional colonoscopy (control group) or AI-assisted colonoscopy (AI group). AI assistance was our newly developed AI system for real-time colonoscopic polyp detection. Primary outcome is polyp detection rate (PDR). Secondary outcomes include polyps per positive patient (PPP), polyps per colonoscopy (PPC), and non-first polyps per colonoscopy (PPC-Plus).

resultsA total of 2352 patients were included in the final analysis. Compared with the control, AI group did not show significant increment in PDR (38.8% vs. 36.2%, p = 0.183), but its PPC-Plus was significantly higher (0.5 vs. 0.4, p < 0.05). In addition, AI group detected more diminutive polyps (76.0% vs. 68.8%, p < 0.01) and flat polyps (5.9% vs. 3.3%, p < 0.05). The effects varied somewhat between centers. In further logistic regression analysis, AI assistance independently contributed to the increment of PDR, and the impact was more pronounced for male endoscopists, shorter insertion time but longer withdrawal time, and elderly patients with larger waist circumference.

conclusionThe intervention of AI plays a limited role in overall polyp detection, but increases detection of easily missed polyps; ChiCTR.org.cn number, ChiCTR1800015607.

Indexed as

Artificial IntelligenceColonic PolypsFemaleHumansMaleMiddle AgedProspective StudiesTreatment Outcomeartificial intelligencecancer preventioncolorectal polypsendoscopyimage analysis

Identifiers

PMID34477306
PMCPMC8525182

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.