Deep learning and capsule endoscopy: automatic panendoscopic detection of protruding lesions.

ABSTRACT:

Objective

Capsule endoscopy (CE) provides a minimally invasive exam modality for panendoscopic evaluation of the entire gastrointestinal (GI) tract. However, conventional reading methods can be time-consuming and error-prone. Protruding lesions are a relatively common entity that can be found with a variable incidence and different pathological significance throughout the GI tract. The aim of this study was to develop and test a convolutional neural network (CNN)-based algorithm for panendoscopic automatic detection of protruding lesions on CE exams.

Methods

A multicentric retrospective study was conducted, based on 1245 CE exams. We used a total of 191 455 frames, from six types of CE devices, of which 52 717 had protruding lesions (polyps, epithelial tumours or subepitheli

SUBMITTER: Mascarenhas Saraiva MJ 

PROVIDER: S-EPMC12519340 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature