Artificial Intelligence to Implement Cost-saving Strategies for Colonoscopy Screening Based on in Vivo Prediction of Polyp Histology

Saving by Artificial Intelligence for Virtual Endoscopy Biopsy Artificial Intelligence to Implement Cost-saving Strategies for Colonoscopy Screening Based on in Vivo Prediction of Polyp Histology

This three parallel-arms, randomized, multicenter trial is aimed at investigating the value of AI-assisted optical biopsy for differentiating between neoplastic and non-neoplastic polyps which will lead to the implementation of cost-saving strategies in screening programs. A cost-effectiveness analyses with the use of modern trial emulation analyses of large observational and clinical trial datasets and real-cost data will be conducted. To improve personalized treatment with a novel colonoscopy CADx risk-prediction tool, the investigators will even develop a novel deep learning algorithm for the optical biopsy of the alternative pathway of colorectal cancer carcinogenesis, namely the serrated pathway and develop cost-effectiveness models of AI-assisted optical biopsy in colorectal cancer screening that provides reliable information to identify cancer risk regardless of physicians' skill.

Inclusion Criteria:

• All >40 years-old patients undergoing colonoscopy for selected indications

Exclusion Criteria:

• patients with personal history of CRC, or IBD
• patients affected with 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 who were not able or refused to give informed written consent.