ArticleiGIE : innovation, investigation and insights2023
Novel artificial intelligence-enabled deep learning system to enhance adenoma detection: a prospective randomized controlled study.
Article in iGIE : innovation, investigation and insights, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The role of artificial intelligence in adenoma detection during colonoscopy: a systematic review and meta-analysis of randomized controlled trials-Artificial intelligence and adenoma detection.Techniques in coloproctology · 2026Pooled it
- Comparative Performance of Artificial Intelligence-Based Computer-Aided Detection Systems for Colorectal Polyps: A Systematic Review and Network Meta-Analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Pooled it
- Artificial intelligence-based computer-aided detection systems for adenomas during colonoscopy: a systematic review and Bayesian network meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Several artificial intelligence (AI) systems for polyp detection during colonoscopy have emerged in the gastroenterology literature and continue to demonstrate significant improvements in quality outcomes. This study assesses clinical quality outcomes during white-light colonoscopy with and without a novel AI computer-aided detection system, DEtection of Elusive Polyps (DEEP Methods: An unblinded, randomized (1:1), controlled, prospective study was performed at a single ambulatory care endoscopy center under institutional review board approval. Included participants ages 40 to 85 years were scheduled to undergo colonoscopy for screening, surveillance, or symptoms. Exclusion criteria were inflammatory bowel disease, prior colorectal surgery, known polyp referral, pregnancy, inadequate bowel prep, and incomplete colonoscopies. DEEP Results: Mean patient age was 62.4 years (SD, 10.29), and 49% were men. Of 674 colonoscopies analyzed, significant differences were found in the adenoma detection rate (ADR) between the 2 arms of the study, those performed without versus with DEEP Conclusions: Seven enrolled physicians and 5 participating nurses reported a unanimous desire to continue using DEEP
Identifiers
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Registered trials
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.