Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies | Zendy

Markus Brand | Zendy; Joel Troya | Zendy; Adrian Krenzer | Zendy; Costanza De Maria | Zendy; N Mehlhase | Zendy; Sebastian Götze | Zendy; Benjamin Walter | Zendy; Alexander Meining | Zendy; Alexander Hann | Zendy

Open Access

Frame-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.

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