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Objectives: Studies have suggested that computer-aided polyp detection using artificial intelligence improves adenoma identification during colonoscopy. However, its real-world effectiveness remains unclear. Therefore, this study evaluated the usefulness of computer-aided detection during regular surveillance colonoscopy.

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Objectives: Colonoscopy is the gold standard for screening cancer and precancerous lesions in the large intestine. Recently, remarkable advances in artificial intelligence (AI) have led to the development of various computer-aided detection (CADe) systems for colonoscopy. This study aimed to evaluate the usefulness of AI for colonoscopy using CAD-EYE (Fujifilm, Tokyo, Japan) to calculate the adenoma miss rate (AMR).

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Objectives: Colonoscopy surveillance is often performed in post-polypectomy cohorts, likely altering colorectal cancer (CRC) outcomes, but this is often not addressed in CRC incidence analyses. We examined CRC incidence post-endoscopic screening, accounting for surveillance.

Methods: We examined UK Flexible Sigmoidoscopy Screening Trial participants who had no, low-risk, or high-risk (≥10 mm, ≥3 adenomas, adenomas with villous features/high-grade dysplasia) distal polyps at screening.

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Familial adenomatous polyposis (FAP) is an autosomal dominant hereditary disease characterized by the progressive development of multiple adenomatous polyps along the colon. The majority of individuals develop colorectal cancer by the age of 40 within the evolutionary course of the disease. For this reason, screening family members is essential to enable identification, surveillance, and appropriate intervention.

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Introduction Colorectal cancer (CRC) represents a major global health burden, significantly impacting mortality rates and healthcare systems worldwide. CRC screening through colonoscopy enables early detection and removal of precancerous polyps. While standard polypectomy suffices for small polyps, larger ones require endoscopic mucosal resection (EMR).

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