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

NCT05916014 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2024-04-12

No results posted yet for this 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.

Conditions

  • Atrophic Gastritis
  • Artificial Intelligence
  • Endoscopy

Interventions

DIAGNOSTIC_TEST

Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists

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

Sponsors & Collaborators

  • Linyi County People's Hospital,Dezhou,China

    collaborator UNKNOWN

  • Shandong University

    lead OTHER

Principal Investigators

  • yanqing li, MD,PHD · Qilu Hospital, Shandong University

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-06-01
Primary Completion
2024-12-31
Completion
2024-12-31

Countries

  • China

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05916014 on ClinicalTrials.gov