Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions.

ABSTRACT:

Background

The efficiency of artificial intelligence as computer-aided detection (CADe) systems for colorectal polyps has been demonstrated in several randomized trials. However, CADe systems generate many distracting detections, especially during interventions such as polypectomies. Those distracting CADe detections are often induced by the introduction of snares or biopsy forceps as the systems have not been trained for such situations. In addition, there are a significant number of non-false but not relevant detections, since the polyp has already been previously detected. All these detections have the potential to disturb the examiner's work.

Objectives

Development and evaluation of a convolutional neuronal network that recognizes instruments in the endoscopic image, sup

SUBMITTER: Brand M 

PROVIDER: S-EPMC9189459 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature