Colorectal Neoplasms, Colorectal Cancer, Adenoma Colon Polyp
Conditions
Keywords
Colorectal cancer screening, Colonoscopy, Artificial Intelligence, CADe, CADx
Brief summary
The goal of this clinical trial is to determine whether using artificial intelligence (AI) can improve the detection and characterization of abnormal growths (polyps) during colonoscopy in adults aged 50 to 74 years who are undergoing colorectal cancer screening after a positive stool test. The main questions it aims to answer are: * Does AI assistance increase the detection of adenomas or advanced colorectal neoplasia? * Does AI provide more accurate optical diagnosis of polyps compared to standard assessment by endoscopists? Researchers will compare colonoscopies performed with AI assistance (using the CAD EYE™ system) to standard colonoscopies without AI to see if AI improves detection rates or diagnostic accuracy. Participants will: * Undergo a screening colonoscopy after a positive fecal immunochemical test (FIT) * Be randomly assigned to either an AI-assisted or standard colonoscopy group * Have any detected polyps removed and analyzed * Receive either AI-based or physician-based optical diagnosis of polyps during the procedure This study helps evaluate whether AI can make colonoscopies more effective and reduce unnecessary polyp removals.
Interventions
DIAGNOSTIC_TESTArtificial Intelligence-Assisted Colonoscopy
The intervention involves the use of an artificial intelligence tool during screening colonoscopy. This system includes two integrated functions: * CADe (Computer-Aided Detection): Highlights suspected lesions in real time on the endoscopic video to assist in identifying polyps. * CADx (Computer-Aided Diagnosis): Provides real-time optical histology predictions to help distinguish between hyperplastic and adenomatous polyps. The AI system operates autonomously during the procedure and displays visual cues on the monitor to support the endoscopist in detecting and characterizing colorectal lesions.
Sponsors
Javier Santos Fernández
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
TRIPLE (Subject, Investigator, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
50 Years to 74 Years
Healthy volunteers
Yes
Inclusion criteria
* Adults aged 50 to 74 years * Positive fecal immunochemical test (FIT) result (≥100 ng/mL) * Scheduled for screening colonoscopy within a population-based colorectal cancer screening program * Able and willing to provide written informed consent
Exclusion criteria
* Incomplete colonoscopy (e.g., failure to reach the cecum) * Inadequate bowel preparation * History of colorectal surgery * Inability to provide informed consent
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| To compare the adenoma detection rate (ADR) and advanced colorectal neoplasia detection rate between conventional colonoscopy and AI-assisted colonoscopy. | During the screening colonoscopy visit (single time point assessment on the day of the procedure). |
Secondary
| Measure | Time frame |
|---|---|
| To compare mean number of lesions between conventional colonoscopy and AI-assisted colonoscopy. | During the screening colonoscopy visit (single time point assessment on the day of the procedure). |
Countries
Spain
Outcome results
None listed