Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos.

ABSTRACT:

Background and aim

Magnifying image-enhanced endoscopy was demonstrated to have higher diagnostic accuracy than white-light endoscopy. However, differentiating early gastric cancers (EGCs) from benign lesions is difficult for beginners. We aimed to determine whether the computer-aided model for the diagnosis of gastric lesions can be applied to videos rather than still images.

Methods

A total of 719 magnifying optical enhancement images of EGCs, 1,490 optical enhancement images of the benign gastric lesions, and 1,514 images of background mucosa were retrospectively collected to train and develop a computer-aided diagnostic model. Subsequently, 101 video segments and 671 independent images were used for validation, and error frames were labeled to retrain the model. Finally,

SUBMITTER: Ma M 

PROVIDER: S-EPMC9389533 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature