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

NCT07208864 | Not Yet Recruiting

• 1 other identifier: 2025SDU-QILU-4
• Study Type: interventional
• Target: 8
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Gastric Intestinal Metaplasia

Outcome Measures

Primary Outcomes (1)

• Efficacy of the EGGIM-AI system for improving endoscopists' EGGIM scoring performance

  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 as the gold standard.

  Through study completion, an average of 3 months

Secondary Outcomes (1)

• Performance of EGGIM scoring by endoscopists with varying experience levels

  Through study completion, an average of 3 months

Study Arms (2)

AI-assisted scoring first group

EXPERIMENTAL

Endoscopists will first evaluate the videos with the assistance of the AI system via EGGIM score, and after the washout period, the videos will be evaluated again without the assistance of the AI system.

Diagnostic Test: AI-assisted EGGIM scoring

Conventional scoring first group

ACTIVE COMPARATOR

Endoscopists will first evaluate the videos without the assistance of the AI system via EGGIM score, and after the washout period, the videos will be evaluated again with the assistance of the AI system.

Diagnostic Test: Conventional EGGIM scoring

Interventions

Conventional EGGIM scoring DIAGNOSTIC_TEST

Endoscopists will evaluate the videos without the assistance of the AI system via EGGIM score.

Conventional scoring first group

AI-assisted EGGIM scoring DIAGNOSTIC_TEST

Endoscopists will evaluate the videos with the assistance of the AI system via EGGIM score.

AI-assisted scoring first group

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Endoscopists who have performed ≥50 image-enhanced endoscopy (IEE) procedures per year and demonstrated competency in performing standardized IEE.

You may not qualify if:

• Endoscopists who participated in data acquisition or were unblinded to patients' identifiable information and clinical data.

Sponsors & Collaborators

• Qilu Hospital of Shandong University: lead

Study Sites (1)

Qilu Hospital of Shandong University

Jinan, Shandong, 250012, China

Location

Central Study Contacts

Zhen Li

CONTACT

86+18560086106; qilulizhen@sdu.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 28, 2025
• First Posted: October 6, 2025
• Study Start: November 30, 2025
• Primary Completion (Estimated): May 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: December 5, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)