RecruitingNCT05916014

AI-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis

Artificial Intelligence-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis to Achieve Gastric Cancer Risk Assessment

Sponsor

Shandong University

Enrollment

1,500 participants

Start Date

Jun 1, 2023

Study Type

OBSERVATIONAL

Conditions

Artificial Intelligence Atrophic Gastritis Endoscopy

Summary

Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification.

Eligibility

Min Age: 18 YearsMax Age: 80 Years

Plain Language Summary

Simplified for easier understanding

This study is testing an AI system to help doctors more accurately classify the severity of atrophic gastritis (stomach lining damage often caused by H. pylori infection) during a standard upper endoscopy. **You may be eligible if...** - You are 18–80 years old - You are scheduled for a standard upper endoscopy (gastroscopy) using white light - You are willing to give informed consent **You may NOT be eligible if...** - You have serious heart, lung, brain, or kidney problems that make endoscopy unsafe - You have known progressive gastric cancer - You have previously had gastric surgery - You are taking medications that could affect the stomach lining results Talk to your doctor to see if this trial is right for you.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

DIAGNOSTIC_TESTDiagnostic Test: The diagnosis of Artificial Intelligence and endosopists

Endosopists and AI will assess the Kimura-Takemoto classification independently when the patients is eligible.

Locations(1)

Department of Gastrology, QiLu Hospital, Shandong University

Shangdong, Shandong, China

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NCT05916014

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