Clinical trial · Interventional
Usefulness of GI-GENIUS in FIT-based Colorectal Cancer Screening Program.
Usefulness of GI-GENIUS in FIT-based Colorectal Cancer Screening Program. Randomized Controlled Trial.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Deep learning technology has an increasing role in medical image applications and, recently, an artificial intelligence device has been developed and commercialized by Medtronic for identification of polyps during colonoscopy (GI-GENIUS). This kind of computer-aided detection (CADe) devices have demonstrated its ability for improving polyp detection rate (PDR) and the adenoma detection rate (ADR). However, this increase in PDR and ADR is mainly made at the expense of small polyps and non advanced adenomas. Colonoscopies after a positive fecal immunochemical test (FIT) could be the scenario with a higher prevalence of advanced lesions which could be the ideal situation for demonstrating if these CADe systems are able also to increase the detection of advanced lesions and which kind of advanced lesions are these systems able to detect. The CADILLAC study will randomize individuals within the population-based Spanish colorectal cancer screening program to receive a colonoscopy where the endoscopist is assisted by the GI-GENIUS device or to receive a standard colonoscopy. If our results are positive, that could suppose a big step forward for CADe devices, in terms of definitive demonstration of being of help for efectively identify also advanced lesions.
Conditions
Conditions (8)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Adenoma Colon | — | UNRESOLVED | — |
| Colonic Polyp | — | UNRESOLVED | — |
| Colorectal Cancer | Malignant Colorectal Neoplasm | CURATED_BROADER | 0.80 |
| Colorectal Neoplasms | Colorectal Neoplasm | ONTOLOGY_EXACT | 0.98 |
| Gastrointestinal Neoplasms | Digestive System Neoplasm | ALIAS | 0.90 |
| Intestinal Neoplasms | Intestinal Neoplasm | ONTOLOGY_EXACT | 0.98 |
| Serrated Adenoma | Traditional Serrated Adenoma | ALIAS | 0.90 |
| Serrated Polyp | — | UNRESOLVED |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Colonoscopy | Other | — | UNRESOLVED |
| GI-GENIUS Medtronic | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- Colonoscopy assisted by GI-GENIUS
- interventionNames
- Device: GI-GENIUS Medtronic
- type
- PLACEBO_COMPARATOR
- label
- Standard colonoscopy
- interventionNames
- Other: Colonoscopy
Primary outcomes (1)
- measure
- Detection of advanced lesions
- timeFrame
- 15 months
- description
- To determine the impact of the GI-GENIUS device as an assistant to the endoscopist to detect advanced lesions (advanced adenomas and advanced serrated polyps) in FIT-based screening colonoscopies.
Secondary outcomes (2)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Individuals with a positive result in fecal immunochemical test within the population-based colorectal cancer screening program. * Complete colonoscopy with cecal intubation. * Inform consent signed. Exclusion Criteria: * Personal history of colorectal cancer. * Family history of colorectal cancer: ≥2 FDR or ≥1 FDR diagnosed before 50 years of age. * Family history of hereditary colorectal cancer syndromes: Lynch syndrome, FAP, etc. * Personal history of inflammatory bowel disease. * Terminal illness. * Personal history of total proctocolectomy.
References
Publications (5)
- RESULTAziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis. J Gastroenterol Hepatol. 2020 Oct;35(10):1676-1683. doi: 10.1111/jgh.15070. Epub 2020 Apr 26. PMID 32267558
- RESULTUrban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18. PMID 29928897
- RESULTWang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019 Oct;68(10):1813-1819. doi: 10.1136/gutjnl-2018-317500. Epub 2019 Feb 27. PMID 30814121
- RESULTWang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, Lei L, Li L, Guo Z, Lei S, Xiong F, Wang H, Song Y, Pan Y, Zhou G. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):343-351. doi: 10.1016/S2468-1253(19)30411-X. Epub 2020 Jan 22. PMID 31981517
- DERIVEDMangas-Sanjuan C, de-Castro L, Cubiella J, Diez-Redondo P, Suarez A, Pellise M, Fernandez N, Zarraquinos S, Nunez-Rodriguez H, Alvarez-Garcia V, Ortiz O, Sala-Miquel N, Zapater P, Jover R; CADILLAC study investigators. Role of Artificial Intelligence in Colonoscopy Detection of Advanced Neoplasias : A Randomized Trial. Ann Intern Med. 2023 Sep;176(9):1145-1152. doi: 10.7326/M22-2619. Epub 2023 Aug 29. PMID 37639723