Screening Colonoscopy
Conditions
Brief summary
The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
Interventions
DEVICEComputer Algorithm
This device is a computer algorithm that runs in the background during routine screening or surveillance colonoscopy that is designed to aid in the detection of polyps
Sponsors
NYU Langone Health
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 99 Years
Healthy volunteers
No
Inclusion criteria
* Patients presenting for routine colonoscopy for screening and/or surveillance purposes. * Ability to provide written, informed consent and understand the responsibilities of trial participation
Exclusion criteria
* People with diminished cognitive capacity. * The subject is pregnant or planning a pregnancy during the study period. * 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 with any polypoid/ulcerated lesion \> 20mm concerning for invasive cancer on endoscopy.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Adenoma Detection Rate | 1 Day | the proportion of colonoscopic examinations performed that detect one or more polyp |
Countries
United States
Outcome results
None listed