Clinical trial · Interventional
Interest of Artificial Intelligence in Cancer Screening Colonoscopy
Cross-sectional, Multi-center Study Comparing Diagnostic Performance Between the CAD EYE System and the Physician on Histological Prediction of Colonic Polyps in Screening of Colorectal Cancer by Colonoscopy
- 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)
Artificial Intelligence (AI) to predict the histology of polyps per colonoscopy, offers a promising solution to reduce variation in colonoscopy performance. This new and innovative non-invasive technology will improve the quality of screening colonoscopies, and reduce the costs of colorectal cancer screening. The aim of the study is to performed a cross-sectional, multi-center study evaluating the diagnostic performance of the CAD EYE automatic characterization system for the histology of colonic polyps in colorectal cancer screening colonoscopy.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Colonoscopy | — | UNRESOLVED | — |
| Colorectal Neoplasms | Colorectal Neoplasm | ONTOLOGY_EXACT | 0.98 |
| Deep Learning | — | UNRESOLVED | — |
| Intestinal Polyps | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| screening colonoscopy | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Patients with indication for colonoscopy
- description
- The screening colonoscopy will be performed by an investigator. The automatic detection and characterization system will be activated at the time of descent of the colonoscopy (after caecal intubation), with video recording (image without CAD EYE and image with CAD EYE). The investigator performing the colonoscopy will be blinded by the results of the CAD EYE.
- interventionNames
- Device: screening colonoscopy
Primary outcomes (1)
- measure
- Estimation of the sensitivity of the automatic characterization system CAD EYE.
- timeFrame
- Inclusion date (date of the colonoscopy)
- description
- Estimation of the sensitivity of the automatic characterization system CAD EYE for the diagnosis of the malignant character of colonic polyps. The sensitivity is calculated as the proportion of polyps detected by the CAD EYE characterization system among all the polyps that will be classified as malignant by the pathological analysis.The sensitivity estimate will be accompanied by its two-sided 95% confidence interval according to the exact binomial distribution. The estimated proportion will be compared with the theoretical value of 85% (minimum level from which this new technique is considered to be of interest) by a unilateral Chi2 test if the validity conditions are met or by an exact Fisher test, at 2.5% alpha threshold.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Patients over 18 years of age with indication for colonoscopy as part of a screening colonoscopy, after a positive immunological test, and/or for personal or family history of colon cancer (before 60 years), personal history of colonic adenoma. * Patient with at least one polyp detected, resect and removed during colonoscopy, for histological analysis Exclusion Criteria: * Guardianship or protection, * pregnancy, * not fluent in French or illiterate, * lack of health care
References
Publications (1)
- RESULTMaigne M, Saunier M, Renaudeau V, Dray X, Guilloux A, Cesbron-Metivier E, Olivier A, Rahmi G, Perrod G, Samaha E, Smith D, Rullier A, Zerbib F, Benard A, Berger A. Real-Time Characterization of Colonic Polyps: A Multicenter Prospective Study Evaluating the CAD-EYE System in Screening Colonoscopies. Clin Transl Gastroenterol. 2026 Mar 1;17(3):e00976. doi: 10.14309/ctg.0000000000000976. PMID 41563136