Open AccessFrame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies
Author(s) -
Markus Brand,
Joel Troya,
Adrian Krenzer,
Costanza De Maria,
N Mehlhase,
Sebastian Götze,
Benjamin Walter,
Alexander Meining,
Alexander Hann
Publication year - 2022
Publication title -
digestion
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.882
H-Index - 75
eISSN - 1421-9867
pISSN - 0012-2823
DOI - 10.1159/000525345
Subject(s) - colonoscopy , frame (networking) , computer science , artificial intelligence , frame rate , computer vision , medicine , colorectal cancer , telecommunications , cancer
Computer-aided detection (CADe) helps increase colonoscopic polyp detection. However, little is known about other performance metrics like the number and duration of false-positive (FP) activations or how stable the detection of a polyp is.