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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SCREENING
Masking
NONE
Enrollment
32
colonoscopy without automated polyp detection software
automated polyp detection software
NYU Langone Health
New York, New York, United States
Number of adenomas detected in combination technique compared to adenomas detected in standard technique Measured by Adenoma Miss Rate (AMR)
AMR will be calculated as the number of adenomas detected on the second pass or portion in either group divided by the total number of adenomas detected during both passes.
Time frame: 1 Year
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