Artificial intelligence-assisted endoscopic diagnosis system for diagnosing Helicobacter pylori infection: a multicenter study.

ABSTRACT:

Background

Deep learning algorithm-based artificial intelligence (AI) has significantly advanced the domain of endoscopic diagnosis; however, its utilization for detecting Helicobacter pylori (H. pylori) infections remains constrained. We aimed to develop and validate the AI diagnostic system (HOPE AI) for diagnosing H. pylori infection by analyzing extensive imaging data obtained from clinical endoscopies.

Methods

This multicenter diagnostic study was carried out across seven hospitals in China. Eligible patients were individuals aged 18 years or older who underwent upper gastrointestinal gastroendoscopy. The endoscopic images were randomly allocated (7:3) to the training and internal validation datasets for the development of HOPE AI, utilizing a multi-instance learning (M

SUBMITTER: Hu Y 

PROVIDER: S-EPMC12505616 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature