Evidence map›Paper›PMID 35449442›Full record

ArticleScientific reports2022

Clinical evaluation of a real-time artificial intelligence-based polyp detection system: a US multi-center pilot study.

Susan Y Quan, Mike T Wei, Jun Lee, Raja Mohi-Ud-Din, Radman Mostaghim, Ritu Sachdev, David Siegel, Yishai Friedlander, Shai Friedland

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 3 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

25 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  9. Article
  10. Deep learning model applied to real-time delineation of colorectal polyps.BMC medical informatics and decision making · 2025
    Article
  11. GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification.IEEE journal of biomedical and health informatics · 2025
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  12. Review
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  20. AI co-pilot bronchoscope robot.Nature communications · 2024
    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

9 authors.

Susan Y QuanStanford University, Stanford, CA, USA.
Mike T WeiStanford University, Stanford, CA, USA. mtwei@stanford.edu.
Jun LeeChosun University, Dong-gu, Gwangju, Republic of Korea.
Raja Mohi-Ud-DinGreenbelt Endoscopy Center, Lanham, MD, USA.
Radman MostaghimGreenbelt Endoscopy Center, Lanham, MD, USA.
Ritu SachdevGreenbelt Endoscopy Center, Lanham, MD, USA.
David SiegelGreenbelt Endoscopy Center, Lanham, MD, USA.
Yishai FriedlanderStatistX, Beer Tuvya, Israel.
Shai FriedlandStanford University, Stanford, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has increasingly been employed in multiple fields, and there has been significant interest in its use within gastrointestinal endoscopy. Computer-aided detection (CAD) can potentially improve polyp detection rates and decrease miss rates in colonoscopy. However, few clinical studies have evaluated real-time CAD during colonoscopy. In this study, we analyze the efficacy of a novel real-time CAD system during colonoscopy. This was a single-arm prospective study of patients undergoing colonoscopy with a real-time CAD system. This AI-based system had previously been trained using manually labeled colonoscopy videos to help detect neoplastic polyps (adenomas and serrated polyps). In this pilot study, 300 patients at two centers underwent elective colonoscopy with the CAD system. These results were compared to 300 historical controls consisting of consecutive colonoscopies performed by the participating endoscopists within 12 months prior to onset of the study without the aid of CAD. The primary outcome was the mean number of adenomas per colonoscopy. Use of real-time CAD trended towards increased adenoma detection (1.35 vs 1.07, p = 0.099) per colonoscopy though this did not achieve statistical significance. Compared to historical controls, use of CAD demonstrated a trend towards increased identification of serrated polyps (0.15 vs 0.07) and all neoplastic (adenomatous and serrated) polyps (1.50 vs 1.14) per procedure. There were significantly more non-neoplastic polyps detected with CAD (1.08 vs 0.57, p < 0.0001). There was no difference in ≥ 10 mm polyps identified between the two groups. A real-time CAD system can increase detection of adenomas and serrated polyps during colonoscopy in comparison to historical controls without CAD, though this was not statistically significant. As this pilot study is underpowered, given the findings we recommend pursuing a larger randomized controlled trial to further evaluate the benefits of CAD.

Indexed as

AdenomaColonic PolypsColorectal NeoplasmsArtificial IntelligenceColonoscopyHumansHydrolasesPilot ProjectsProspective StudiesHydrolases

Identifiers

PMID35449442
PMCPMC9023509

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Registered trials

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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.