ArticleDigestive diseases and sciences2024
Performance of Computer-Aided Detection and Quality of Bowel Preparation: A Comprehensive Analysis of Colonoscopy Outcomes.
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. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A randomized control trial to assess the effectiveness of a brief educational intervention on colonoscopy preparation and experience in patients with inflammatory bowel disease.BMC gastroenterology · 2025Trial
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Ethical and Legal Implications of Implementing AI in Gastrointestinal Endoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Review
- Assessment of the impact of power business intelligence on adenoma detection rate: a prospective observational trial.BMC gastroenterology · 2025Observational
- Effectiveness of CADe in Colonoscopy for Improved Adenoma and Polyp Detection Under Diverse Clinical Conditions.In vivo (Athens, Greece)Article
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Authors and funding
13 authors.
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Abstract
backgroundArtificial intelligence (AI) has emerged as a promising tool for detecting and characterizing colorectal polyps during colonoscopy, offering potential enhancements in traditional colonoscopy procedures to improve outcomes in patients with inadequate bowel preparation.
aimsThis study aimed to assess the impact of an AI tool on computer-aided detection (CADe) assistance during colonoscopy in this population.
methodsThis case-control study utilized propensity score matching (PSM) for age, sex, race, and colonoscopy indication to analyze a database of patients who underwent colonoscopy at a single tertiary referral center between 2017 and 2023. Patients were excluded if the procedure was incomplete or aborted owing to poor preparation. The patients were categorized based on the use of AI during colonoscopy. Data on patient demographics and colonoscopy performance metrics were collected. Univariate and multivariate logistic regression models were used to compare the groups.
resultsAfter PSM patients with adequately prepped colonoscopies (n = 1466), the likelihood of detecting hyperplastic polyps (OR = 2.0, 95%CI 1.7-2.5, p < 0.001), adenomas (OR = 1.47, 95%CI 1.19-1.81, p < 0.001), and sessile serrated polyps (OR = 1.90, 95%CI 1.20-3.03, p = 0.007) significantly increased with the inclusion of CADe. In inadequately prepped patients (n = 160), CADe exhibited a more pronounced impact on the polyp detection rate (OR = 4.34, 95%CI 1.6-6.16, p = 0.049) and adenomas (OR = 2.9, 95%CI 2.20-8.57, p < 0.001), with a marginal increase in withdrawal and procedure times.
conclusionThis study highlights the significant improvement in detecting diminutive polyps (< 5 mm) and sessile polyps using CADe, although notably, this benefit was only seen in patients with adequate bowel preparation. In conclusion, the integration of AI in colonoscopy, driven by artificial intelligence, promises to significantly enhance lesion detection and diagnosis, revolutionize the procedure's effectiveness, and improve patient outcomes.
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