Clinical trial · Interventional
Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
NCT04586556CI-TRIAL-00062345completedN/AClinicalTrials.gov clinicaltrialsProvenance
- 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)
The investigators hypothesize that the clinical implementation of a deep learning AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps and other anatomical landmarks during colonoscopy. The objectives of this study are to generate preliminary data to evaluate the effectiveness of AI-assisted colonoscopy on: a) the rate of detection of adenomas; b) the automatic detection of the anatomical landmarks (i.e., ileocecal valve and appendiceal orifice).
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Adenomatous Polyps | Adenomatous Polyp | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Polyps detection by Artificial Intelligence | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Artificial intelligence for real-time detection and monitoring of colorectal polyps
- description
- A standard colonoscopy will be performed according to the standard of routine care. All optically diagnosed polyps will be removed and sent to the CHUM pathology laboratory for histopathological evaluation according to institutional standards. The AI system will capture video of the procedure in real time, and provide additional information on the detection of polyps, follow-up and prediction of pathology. The full-length colonoscopy videos will be annotated for the exact time of the identification of the anatomical landmarks, polyps, also for polyp- and procedural-related characteristics.
- interventionNames
- Diagnostic Test: Polyps detection by Artificial Intelligence
Primary outcomes (2)
- measure
- Number of polyps detected
- timeFrame
- Day 1
- description
- Efficacy of AI assisted colonoscopy to detect the proportion of patients with at least 1 polyp. Polyp detection rate with an AI.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 45 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria : * Signed informed consent * Age 45-80 years * Indication to undergo a lower GI endoscopy. Exclusion Criteria : * Coagulopathy * Poor general health, defined as an American Society of Anesthesiologists (ASA) physical status class \>3 * Emergency colonoscopies * Hospitalized patients * Known inflammatory bowel disease (IBD) * Patients currently in the emergency room
References
Publications (0)
Data not yet available
No reference posted for this study.