Automated Detection of Anatomical Landmarks During Colonoscopy Using a Deep Learning Model.

ABSTRACT:

Background and aims

Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm completeness of colonoscopy examinations. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps.

Methods

We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three nonoverlapping training, validation, and test data sets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). A DCNN classification model was developed, validated, and tested in separate

SUBMITTER: Taghiakbari M 

PROVIDER: S-EPMC10395661 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature