NCT05916014

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,500 (estimated)
• Sponsor: Shandong University · Academic / Other
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Not accepted

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.

Detailed 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.

Conditions

• Atrophic Gastritis
• Artificial Intelligence
• Endoscopy

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2023-06-01
• Primary completion: 2024-12-31
• Completion: 2024-12-31
• First posted: 2023-06-23
• Last updated: 2024-04-12

Locations

1 site across 1 country: China