Colorectal Cancer, Colorectal Neoplasms
Conditions
Keywords
artificial intelligence
Brief summary
This study assesses the sensitivity and added benefits of computer-aided detection compared to standard care (white-light) in detecting colon polyps in patients undergoing colonoscopy.
Detailed description
Failure in polyp detection leads to colon cancer after colonoscopy. Artificial intelligence systems allow real-time computer-aided detection of polyps with high-accuracy. This study will compare GI-Genius, a real-time CAD system to standard colonoscopy in terms of how many colonoscopies detect an adenoma.
Interventions
DEVICEReal-Time Computer Aided Detection
This intervention involves using computer-aided detection using a real-time system (GI-Genius, Medtronic)
PROCEDUREStanford Colonoscopy
This involves white-light colonoscopy
Sponsors
Riverside University Health System Medical Center
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
30 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
* Undergoing colonoscopy at RUHS * Age \> 45 years * No contraindications to colonoscopy
Exclusion criteria
* Prior history of subtotal colectomy
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Adenoma Detection Rate | 1 year |
Secondary
| Measure | Time frame |
|---|---|
| Adenomas Per Colon | 1 year |
| Sessile Serrated Lesion Detection Rate | 1 year |
| Sessile Serrated Lesions Per Colon | 1 year |
| False Neoplasia Rate | 1 year |
| Withdrawal Time | 1 year |
Countries
United States
Outcome results
None listed