Efficacy of an AI System in Training Endoscopists to Assess Gastric Intestinal Metaplasia Via the EGGIM Score

This prospective randomized controlled trial with a crossover design incorporated image-enhanced endoscopy (IEE) videos demonstrating complete standardized examinations of five standard gastric areas (antrum greater curvature, antrum lesser curvature, incisura, corpus lesser curvature, and corpus greater curvature). Endoscopists were stratified by experience level and randomly assigned to either the AI-assisted scoring first group, which performed EGGIM scoring with AI assistance in the initial phase followed by conventional scoring after a washout period, or the conventional scoring first group, which completed the assessments in reverse order. The study primarily evaluated the training efficacy of the EGGIM-AI system for improving endoscopists' EGGIM scoring performance by comparing diagnostic accuracy metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity between groups at different study phases, with histopathological results serving as the gold standard.

NCT Number	NCT07208864
Lead Sponsor	Qilu Hospital of Shandong University
Conditions	Gastric Intestinal Metaplasia
Enrollment	8 participants
Start Date	2025-11-30
Primary Completion	2026-05-31 (estimated)
Study Completion	2026-12-31 (estimated)
Updated on ClinicalTrials.gov	2025-12-05