Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience.

ABSTRACT:

Aim

The use of artificial intelligence represents an objective approach to increase endoscopist's adenoma detection rate (ADR) and limit interoperator variability. In this study, we evaluated a newly developed deep convolutional neural network (DCNN) for automated detection of colorectal polyps ex vivo as well as in a first in-human trial.

Methods

For training of the DCNN, 116 529 colonoscopy images from 278 patients with 788 different polyps were collected. A subset of 10 467 images containing 504 different polyps were manually annotated and treated as the gold standard. An independent set of 45 videos consisting of 15 534 single frames was used for ex vivo performance testing. In vivo real-time detection of colorectal polyps during routine colonoscopy by the DCNN was teste

SUBMITTER: Pfeifer L 

PROVIDER: S-EPMC8734627 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature