Artificial intelligence efficiently predicts gastric lesions, <i>Helicobacter pylori</i> infection and lymph node metastasis upon endoscopic images.

ABSTRACT:

Objective

Medical images have been increased rapidly in digital medicine era, presenting an opportunity for the intervention of artificial intelligence (AI). In order to explore the value of convolutional neural network (CNN) algorithms in endoscopic images, we developed an AI-assisted comprehensive analysis system for endoscopic images and explored its performance in clinical real scenarios.

Methods

A total of 6,270 white light endoscopic images from 516 cases were used to train 14 different CNN models. The images were divided into training set, validation set and test set according to 7:1:2 for exploring the possibility of discrimination of gastric cancer (GC) and benign lesions (nGC), gastric ulcer (GU) and ulcerated cancer (UCa), early gastric cancer (EGC) and nGC, infec

SUBMITTER: Yang R 

PROVIDER: S-EPMC11555197 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature