ReviewWorld journal of gastroenterology2020
Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped.
Review in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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
22 citing papers in PubMed, 42 citations in OpenAlex.
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Artificial Intelligence in Colonoscopy: A Systematic Review of Adenoma Versus Polyp Detection Rates.Cureus · 2025Review
- Advantages of new generation colonoscopes on adenoma detection: A propensity-score matching study.World journal of gastrointestinal endoscopy · 2025Article
- Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.World journal of gastroenterology · 2025Article
- Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor.Diagnostics (Basel, Switzerland) · 2025Article
- A Narrative Review on the Role of Artificial Intelligence (AI) in Colorectal Cancer Management.Cureus · 2025Review
- Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.Journal of clinical medicine · 2025Review
- Performance of Computer-Aided Detection and Quality of Bowel Preparation: A Comprehensive Analysis of Colonoscopy Outcomes.Digestive diseases and sciences · 2024Article
- Could atmospheric temperature impact on adequate colon cleansing for colonoscopy? An observational, single-institution study.International journal of colorectal disease · 2023Observational
- Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening.Diagnostics (Basel, Switzerland) · 2023Review
- Stature estimation by semi-automatic measurements of 3D CT images of the femur.International journal of legal medicine · 2023Article
- Endoscopic Ultrasound Advanced Techniques for Diagnosis of Gastrointestinal Stromal Tumours.Cancers · 2023Review
- Artificial intelligence and inflammatory bowel disease: Where are we going?World journal of gastroenterology · 2023Review
- Evaluation of artificial intelligence techniques in disease diagnosis and prediction.Discover artificial intelligence · 2023Review
- Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists.Endoscopy international open · 2022Article
- Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do.World journal of gastroenterology · 2022Review
- Article
- Texture and color enhancement imaging in magnifying endoscopic evaluation of colorectal adenomas.World journal of gastrointestinal endoscopy · 2022Article
- Does Fellow Participation Increase the Adenoma Detection Rate?Digestive diseases and sciences · 2022Article
- How to trick artificial intelligence: rectal heterotopic gastric lateral spreading tumor.VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or pre-cancerous lesions and the capacity to remove them intra-procedurally. Computer-aided detection and diagnosis (CAD), thanks to the brand new developed innovations of artificial intelligence, and especially deep-learning techniques, leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy. The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate, and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality. Furthermore, a significant reduction in costs is also expected. In addition, the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule. The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy, as it is reported in literature, addressing evidence, limitations, and future prospects.
Indexed as
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What OpenQuestion holds
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.