Evidence map›Paper›PMID 34762157›Full record

SynthesisInternational journal of colorectal disease2022

Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials.

Ding Huang, Jingyi Shen, Jiaze Hong, Yi Zhang, Senjie Dai, Nannan Du, Mengting Zhang, Daxin Guo

Abstract readMeta-AnalysisReview
PubMed Publisher
In one paragraph

Synthesis in International journal of colorectal disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 4 pooled it
3.5field-weighted citation impact, top 5% 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.

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

34 citing papers in PubMed, 4 syntheses or guidelines pooled it, 57 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Quality indicators for colonoscopy.Gastrointestinal endoscopy · 2024
    Guideline
  5. Trial
  6. Trial
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  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
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  16. [Role of Artificial Intelligence in Improving Quality of Colonoscopy].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025
    Review
  17. Article
  18. Review
  19. Quality Indicators for Colonoscopy.The American journal of gastroenterology · 2024
    Article
  20. Article
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

8 authors at 2 institutions in 1 country.

Ding Huang *Medical Department, Ningbo Anorectal Hospital, Ningbo, Zhejiang, China.
Jingyi Shen *The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Jiaze HongThe Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Yi ZhangThe Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Senjie DaiThe Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Nannan DuThe Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Mengting ZhangThe Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Daxin GuoDepartment of Gastroenterology, HwaMei Hospital, University of Chinese Academy of Sciences, Haishu District, Northwest Street 41, Ningbo, 315010, Zhejiang, China. airgzn666@163.com.
Zhejiang Chinese Medical University · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis meta-analysis aimed to determine whether artificial intelligence (AI) improves colonoscopy outcome metrics i.e. adenoma detection rate (ADR) and polyp detection rate (PDR).

methodsTwo authors independently searched Web of Science, PubMed, Science Direct, and Cochrane Library to find all published research before July 2021 that has compared AI-aided colonoscopy with routine colonoscopy (RC) for detection of adenoma and polyp.

resultsThis meta-analysis included 10 RCTs with 6629 individuals in AI-aided (n = 3300) and routine (n = 3329) groups. The results showed that both ADR (RR, 1.43; P < 0.001) and PDR (RR, 1.44; P < 0.001) using AI-aided endoscopy were significantly greater when compared with RC. The adenomas detected per colonoscopy (APC) (WMD, 0.25; P = 0.009), polyps detected per colonoscopy (PPC) (WMD, 0.52; P < 0.001), and sessile serrated lesions detected per colonoscopy (SSLPC) (RR, 1.53; P < 0.001) were significantly higher in the AI-aided group compared with the RC group. Subgroup analysis based on size, location, and shape of adenomas and polyps demonstrated that, except for in the cecum and pedunculated adenomas or polyps, the AI-aided groups of the other subgroups are more advantageous. Withdrawal time was longer in the AI-aided group when biopsies were included, while withdrawal time excluding biopsy time showed no significant difference.

conclusionsAI-aided polyp detection system significantly increases lesion detection rate. In addition, lesion detection by AI is hardly affected by factors such as size, location, and shape.

Indexed as

AdenomaColonic PolypsColorectal NeoplasmsArtificial IntelligenceColonoscopyHumansRandomized Controlled Trials as TopicAdenoma detectionArtificial intelligenceColonoscopyMeta-analysisPolyp detection

Identifiers

PMID34762157
OpenAlexW3213426728

What OpenQuestion holds

Textmetadata
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.