The CERTAIN Study: Combining Endo-cuff in a Randomized Trial for Artificial Intelligence Navigation
Condition: Artificial Intelligence
Study type: Observational

Summary: 
      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.
    


Intervention name(s): Artificial Intelligence
Intervention type(s): Other
Intervention description(s): Artificial intelligence
Observational model: Cohort
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        All 40-80 years-old subjects undergoing a colonoscopy for gastrointestinal symptoms, fecal
        immunohistochemical test positivity, primary screening or post-polypectomy surveillance.