Computer Aided Detection, Tandem Colonoscopy Study
Completed · Not applicable
Conditions studied: Colonic Diseases
In brief
The purpose of this project is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine in real-time how a computer-aided detection (CADe) algorithm will perform when compared to standard screening or surveillance colonoscopy alone. Design will be a multi-center, prospective, unblinded randomized tandem colonoscopy study. 196 patients referred for either screening or surveillance colonoscopy will be included.
Key facts
- Study ID
- NCT04074577
- Run by
- NYU Langone Health
- People needed
- 32
- Starts
- 2020-09-17
- Expected to finish
- 2020-10-14
- Last updated by the study team
- 2021-06-29
Who can join
Age: 22 and older. Sex: any. Healthy volunteers: accepted.
You may qualify if…
- Patients presenting to the endoscopy unit for a screening colonoscopy or a surveillance colonoscopy
- Willingness to undergo tandem colonscopies with and without the use of computer-aided software while undergoing conventional colonoscopy with sedation
- Ability to provide written, informed consent (approved by IRB) and understand the responsibilities of trial participation
You may not qualify if…
- People with diminished cognitive capacity
- Patients undergoing diagnostic colonoscopy (e.g. as an evaluation for active GI bleed)
- Patients with incomplete colonoscopies (those where endoscopists did not successfully intubate the cecum due to technical difficulties or poor bowel preparation)
- Patients that have standard contraindications to colonoscopy in general (e.g. documented acute diverticulitis, fulminant colitis and known or suspected perforation).
- Patients with inflammatory bowel disease
- Patients referred for endoscopic mucosal resection (EMR)
Where it is running
- NYU Langone Health — New York, New York, United States
Full record on ClinicalTrials.gov
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