ArticleArquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery2025
Artificial intelligence-assisted colonoscopy for colorectal lesion detection: a case-control study on diagnostic accuracy and histopathological agreement.
Article in Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Prompt engineering in medical education. Dissecting the new technological frontier in Digestive Surgery.Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery · 2026Article
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4 authors.
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Abstract
backgroundArtificial intelligence (AI)-assisted colonoscopy has emerged as a tool to enhance adenoma detection rates (ADRs) and improve lesion characterization. However, its performance in real-world settings, especially in developing countries, remains uncertain.
aimsThe aim of this study was to evaluate the impact of AI on ADRs and its concordance with histopathological diagnosis.
methodsA matched case-control study was conducted at a colorectal cancer (CRC) referral center, including 146 patients aged 45-75 years who underwent colonoscopy for CRC screening or surveillance. Patients were allocated into two groups: AI-assisted colonoscopy (n=74) and high-definition conventional colonoscopy (n=72). The primary outcome was ADR, and the secondary outcome was the agreement between AI-based lesion characterization and histopathology. Statistical analysis was performed with a significance level of p<0.05.
resultsADR was higher in the AI group (60%) than in the control group (50%), but this difference was not statistically significant (p>0.05). AI-assisted lesion characterization showed substantial agreement with histopathology (kappa=0.692). No significant difference was found in withdrawal time (29 min vs. 27 min; p>0.05), indicating that AI did not delay the procedure.
conclusionsAlthough AI did not significantly increase ADR compared to conventional colonoscopy, it demonstrated strong histopathological concordance, supporting its reliability in lesion characterization. AI may reduce interobserver variability and optimize real-time decision-making, reinforcing its clinical utility in CRC screening.
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