Artificial Intelligence for Determination of Gastroscopy Surveillance Intervals

Study Overview
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Brief Summary
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The purpose of this study is to develop and validate a clinical decision support system based on automated algorithms. This system can use natural language processing to extract data from patients' endoscopic reports and pathological reports, identify patients' disease types and grades, and generate guidelines based follow-up or treatment recommendations

Official Title
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Development and Validation of Gastroscopy Surveillance Recommendations Based on Natural Language Processing for Patients With Gastric Cancer and Precancerous Diseases

Conditions
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Helicobacter Pylori Infection, Atrophic Gastritis, Intestinal Metaplasia, Low Grade Intraepithelial Neoplasia, High Grade Intraepithelial Neoplasia, Early Gastric Cancer, Gastric Cancer

Intervention / Treatment
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* Other: AI recongnize disease and generate recommendations


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients aged 18 - 80 years Patients underwent endoscopic examination Exclusion Criteria: Patients with the contraindications to endoscopic examination Patients with imcomplete examination information Patients undergo endoscopy for therapy Patients have history of upper gastrointestinal surgery Patients with duodenal or Laryngeal neoplasms Patients with gastrointestinal submucosal tumor

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Artificial Intelligence support decision group<br>According the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations. | Other: AI recongnize disease and generate recommendations<br>* According the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The diagnostic accuracy of gastric diseases with deep learning algorithm | The diagnostic accuracy of gastric diseases with deep learning algorithm | 12 month | 
| The accuracy of recommentions for different disease with deep learning algorithm | The accuracy of recommentions for different disease with deep learning algorithm | 12 month | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The diagnostic sensitivity of gastric diseases with deep learning algorithm | The diagnostic sensitivity of gastric diseases with deep learning algorithm | 12 month | 
| The diagnostic specificity of gastric diseases with deep learning algorithm | The diagnostic specificity of gastric diseases with deep learning algorithm | 12 month | 
| The diagnostic positive predictive value of gastric diseases with deep learning algorithm | The diagnostic positive predictive valu of gastric diseases with deep learning algorithm | 12 month | 
| The diagnostic negative predictive value of gastric diseases with deep learning algorithm | The diagnostic negative predictive value of gastric diseases with deep learning algorithm | 12 month | 
| The F-score of gastric diseases with deep learning algorithm | The F-score of gastric diseases with deep learning algorithm | 12 month |