Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence | — | UNRESOLVED | — |
| Atrophic Gastritis | — | UNRESOLVED | — |
| Endoscopy | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Chronic atrophic gastritis observed by white light endoscope
- description
- Get pictures from gastric antrum,gastric angle,lesser curvature of gastric body, cardia, gastric fundus, greater curvature of gastric body by white light endoscope
- interventionNames
- Diagnostic Test: Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists
Primary outcomes (3)
- measure
- Accuracy of AI model to diagnose the Kimura-Takemoto classification
- timeFrame
- 2 years
- description
- Accuracy of AI model to diagnose the Kimura-Takemoto classification
- measure
- Sensitivity of AI model to diagnose the Kimura-Takemoto classification
- timeFrame
- 2 years
- description
- Sensitivity of AI model to diagnose the Kimura-Takemoto classification
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: Patients aged 18-80 years who undergo the white light endoscope examination Informed consent form provided by the patient. 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;
References
Publications (0)
Data not yet available