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| Name | Class |
|---|---|
| Linyi County People's Hospital,Dezhou,China | UNKNOWN |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Chronic atrophic gastritis observed by white light endoscope | Get pictures from gastric antrum,gastric angle,lesser curvature of gastric body, cardia, gastric fundus, greater curvature of gastric body by white light endoscope |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Diagnostic Test: The diagnosis of Artificial Intelligence and endosopists | Diagnostic Test | Endosopists and AI will assess the Kimura-Takemoto classification independently when the patients is eligible. |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of AI model to diagnose the Kimura-Takemoto classification | Accuracy of AI model to diagnose the Kimura-Takemoto classification | 2 years |
| Sensitivity of AI model to diagnose the Kimura-Takemoto classification | Sensitivity of AI model to diagnose the Kimura-Takemoto classification | 2 years |
| Specificity of AI model to diagnose the Kimura-Takemoto classification | Specificity of AI model to diagnose the Kimura-Takemoto classification | 2 years |
| Measure | Description | Time Frame |
|---|---|---|
| The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture | The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture | 2 years |
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Inclusion Criteria:
Patients aged 18-80 years who undergo the white light endoscope examination Informed consent form provided by the patient.
Exclusion Criteria:
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Consecutive patients who receive the gastrointestinal endoscopy examination and screened that fulfill the eligibility criteria at Qilu Hospital,Shandong University,Linyi County People's Hospital will be enrolled into the study
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| yanqing Li, MD, PHD | Contact | 0531182169385 | liyanqing@sdu.edu.cn |
| Name | Affiliation | Role |
|---|---|---|
| yanqing li, MD,PHD | Qilu Hospital of Shandong University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Gastrology, QiLu Hospital, Shandong University | Recruiting | Shangdong | Shandong | 250012 | China |
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| ID | Term |
|---|---|
| D005757 | Gastritis, Atrophic |
| ID | Term |
|---|---|
| D005756 | Gastritis |
| D005759 | Gastroenteritis |
| D005767 | Gastrointestinal Diseases |
| D004066 | Digestive System Diseases |
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| D013272 |
| Stomach Diseases |