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

Last updated: April 10, 2024
Sponsor: Shandong University
Overall Status: Active - Recruiting

Phase

N/A

Condition

Gastrointestinal Diseases And Disorders

Ulcers

Non-ulcer Dyspepsia (Nud)

Treatment

Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists

Clinical Study ID

NCT05916014
2022SDU-QILU-123
  • Ages 18-80
  • All Genders

Study 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 Criteria

Inclusion

Inclusion Criteria: Patients aged 18-80 years who undergo the white light endoscope examination Informedconsent form provided by the patient.

Exclusion

Exclusion Criteria:

  1. patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric;
  2. disorders who cannot participate in gastroscopy;
  3. Patients with progressive gastric cancer;
  4. low quality pictures;
  5. patients with previous surgical procedures on the stomach or esophageal;
  6. patients who refuse to sign the informed consent form;

Study Design

Total Participants: 1500
Treatment Group(s): 1
Primary Treatment: Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists
Phase:
Study Start date:
June 01, 2023
Estimated Completion Date:
December 31, 2024

Study Description

Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. The higher the score, the more severe the degree of atrophic gastritis. 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 of atrophic gastritis to achieve gastric cancer risk assessment.

Connect with a study center

  • Department of Gastrology, QiLu Hospital, Shandong University

    Shangdong, Shandong 250012
    China

    Active - Recruiting

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