AI Support for Telling Harmless From Cancerous Colon Polyps During Colonoscopy
Tests AI-assisted colonoscopy to tell harmless bowel growths from precancerous ones in adults.
Plain-language title generated from the registry record
Registry title: Artificial Intelligence to Implement Cost-saving Strategies for Colonoscopy Screening Based on in Vivo Prediction of Polyp Histology
Key facts
- Where
- Rozzano, ItalySee the site
Where in Italy
- Istituto Clinico HumanitasRozzano, ItalyTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Istituto Clinico Humanitas
- Trial code
- NCT06041945
- Record updated
- 30 October 2026
- Original description
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.Read full original descriptionShow less
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: All >40 years-old patients undergoing colonoscopy for selected indications.
Exclusion: patients with previous colonic resection; patients on antithrombotic therapy, precluding polyp resection; 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 who were not able or refused to give informed written consent.