Expired Study
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New York, New York 10016


Purpose:

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.


Criteria:

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.


NCT ID:

NCT03637712


Primary Contact:

Principal Investigator
Seth Gross, MD
NYU Langone Health


Backup Contact:

N/A


Location Contact:

New York, New York 10016
United States



There is no listed contact information for this specific location.

Site Status: N/A


Data Source: ClinicalTrials.gov

Date Processed: October 09, 2019

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