Evidence map›Paper›PMID 38436866›Full record

ArticleDigestive diseases and sciences2024

Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis.

Manesh Kumar Gangwani, Hossein Haghbin, Rizwan Ishtiaq, Fariha Hasan, Julia Dillard, Fouad Jaber, Dushyant Singh Dahiya, Hassam Ali, Shaharyar Salim, Wade Lee-Smith and 4 more

Erratum issuedAbstract readNetwork Meta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Article in Digestive diseases and sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Review
  4. Article
  5. Review
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Manesh Kumar GangwaniDepartment of Medicine, University of Toledo Medical Center, Toledo, OH, USA. gangwani.manesh@gmail.com.
Hossein HaghbinDepartment of Gastroenterology and Hepatology, Ascension Providence Hospital, Southfield, MI, USA.
Rizwan IshtiaqDepartment of Medicine, St Francis Hospital and Medical Center, Hartford, CT, USA.
Fariha HasanDepartment of Internal Medicine, Cooper University Hospital, Camden, NJ, USA.
Julia DillardDepartment of Medicine, University of Toledo Medical Center, Toledo, OH, USA.
Fouad JaberDepartment of Internal Medicine, University of Missouri-Kansas City, Kansas City, MO, USA.
Dushyant Singh DahiyaDepartment of Medicine, Central Michigan University College of Medicine, Mount Pleasant, MI, USA.
Hassam AliDepartment of Gastroenterology and Hepatology, East Carolina University Health, Greenville, NC, USA.
Shaharyar SalimDepartment of Internal Medicine, Aga Khan University Hospital, Karachi, Pakistan.
Wade Lee-SmithUniversity of Toledo Libraries, University of Toledo, Toledo, OH, USA.
Amir Humza SohailDepartment of General Surgery, New York University Langone Health, Long Island, NY, USA.
Sumant InamdarDepartment of Gastroenterology and Hepatology, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Muhammad AzizDepartment of Gastroenterology and Hepatology, University of Toledo Medical Center, Toledo, OH, USA.
Benjamin HartDepertment of Hepatology and Gastroenterology, University of Michigan, Ann Arbor, MI, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsScreening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR.

methodsWe searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant.

resultsWe analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595-0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688-0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9-1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer.

conclusionArtificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.

Indexed as

AdenomaColorectal NeoplasmsArtificial IntelligenceColonoscopyHumansOdds RatioAdenoma detection rateArtificial intelligenceColorectal cancerSecond observerSingle observer

Identifiers

PMID38436866
PMCPMC11026252

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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.