Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC. Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC.

Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have permitted to develop several AI platforms which have already proved their efficacy in increasing adenoma detection during colonoscopy9,10. As a matter of fact, the improvement in detection due to AI systems is only related to the increased capacity of detecting lesions within the visual field, that is dependent on the amount of mucosa exposed by the endoscopist during the scope withdrawal.

Increasing the mucosa exposure would theoretically be a complementary strategy to further improve polyps detection. A number of distal attachments have been tested to increase the mucosal exposure by flattening mucosal folds, including a transparent cap, cuff or rings. The additional diagnostic yield obtained by the second generation of cuff (Endocuff Vision; Olympus America, Center Valley, Pa, USA) was recently investigated by a meta-analysis of randomized controlled trials, showing a significant improvement in adenoma detection rate, and adenomas per colonoscopy, with a reduction in the mean withdrawal time without any increase in adverse events compared with standard high-definition colonoscopy without any distal attachment.

In conclusion, technologies providing either mucosal image enhancement (Artificial Intelligence assisted colonoscopy) or mucosal exposure device (Endocuff Vision assisted colonoscopy) significantly improved adenoma detection rate (ADR). However, the diagnostic yield obtained by combining the different strategies is still unknown.

Inclusion Criteria:

• subjects undergoing a colonoscopy for gastrointestinal symptoms, fecal immunohistochemical test positivity, primary screening or post-polypectomy surveillance

Exclusion Criteria:

• subjects with personal history of CRC, or IBD.
• subjects affected with genetic mutations such as Lynch syndrome or Familiar Adenomatous Polyposis.
• patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale > 2 in any colonic segment).
• patients with previous colonic resection.
• patients on antithrombotic therapy, precluding polyp resection.
• patients with history of colonic strictures, precluding ECV use.
• patients who were not able or refused to give informed written consent.

All 40-80 years-old subjects undergoing a colonoscopy for gastrointestinal symptoms, fecal immunohistochemical test positivity, primary screening or post-polypectomy surveillance.