Evidence map›Paper›PMID 33294821›Full record

ArticleEClinicalMedicine2020

Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials.

Babu P Mohan, Antonio Facciorusso, Shahab R Khan, Saurabh Chandan, Lena L Kassab, Paraskevas Gkolfakis, Georgios Tziatzios, Konstantinos Triantafyllou, Douglas G Adler

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EClinicalMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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

  1. A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023
    Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Review
  7. Integration of Endocuff-Assisted and Computer-Aided Colonoscopy: A Meta-Analysis of Randomized Controlled Trials.JGH open : an open access journal of gastroenterology and hepatology · 2026
    Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Artificial Intelligence for Colonoscopy: Past, Present, and Future.IEEE journal of biomedical and health informatics · 2022
    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

9 authors.

Babu P MohanGastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT, USA.
Antonio FacciorussoGastroenterology Unit, University of Foggia, Foggia, Italy.
Shahab R KhanGastroenterology, Rush University Medical Center, Chicago, IL, USA.
Saurabh ChandanGastroenterology and Hepatology, CHI Creighton University Medical Center, Omaha, NE, USA.
Lena L KassabInternal Medicine, Mayo Clinic, Rochester, MIN, USA.
Paraskevas GkolfakisHepatogastroenterology Unit, 2nd Dep of Internal Medicine - Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Georgios TziatziosHepatogastroenterology Unit, 2nd Dep of Internal Medicine - Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Konstantinos TriantafyllouHepatogastroenterology Unit, 2nd Dep of Internal Medicine - Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Douglas G AdlerGastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecent prospective randomized controlled trials have evaluated deep convolutional neural network (CNN) based computer aided detection (CADe) of lesions in real-time colonoscopy. We conducted this meta-analysis to compare the adenoma detection rate (ADR) of deep CNN based CADe assisted colonoscopy to standard colonoscopy (SC) from randomized controlled trials (RCTs).

methodsMultiple databases were searched (from inception to May 2020) and parallel RCTs that compared deep CNN based CADe assisted colonoscopy to SC were included for this analysis. Using Mantel-Haenzel (M-H) random effects model, pooled risk ratios (RR) and mean difference (MD) were calculated. In between study heterogeneity was assessed by I

findingsSix RCTs were included in our final analysis that utilized deep CNN based CADe system in real-time colonoscopy. Total numbers of patients assessed were 4962 (2480 in CADe and 2482 in SC group). CADe based colonoscopy demonstrated statistically higher pooled ADR, RR=1.5 (95% CI 1.3-1.72),

interpretationBased on our meta-analysis, deep CNN based CADe colonoscopy achieved significantly higher ADR metrics, albeit with increased scope withdrawal time when compared to SC.

Indexed as

Adenoma detection rateColonoscopyConvolutional neural networks

Identifiers

PMID33294821
PMCPMC7691740

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.