Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study.

ABSTRACT:

Background

The early diagnosis and treatment of Heliobacter pylori (H.pylori) gastrointestinal infection provide significant benefits to patients. We constructed a convolutional neural network (CNN) model based on an endoscopic system to diagnose H. pylori infection, and then examined the potential benefit of this model to endoscopists in their diagnosis of H. pylori infection.

Materials and methods

A CNN neural network system for endoscopic diagnosis of H.pylori infection was established by collecting 7377 endoscopic images from 639 patients. The accuracy, sensitivity, and specificity were determined. Then, a randomized controlled study was used to compare the accuracy of diagnosis of H. pylori infection by endoscopists who were assisted or unassisted by this CNN model.

SUBMITTER: Zou PY 

PROVIDER: S-EPMC11440712 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature