Artificial Intelligence for Determination of Gastroscopy Surveillance Intervals
Study Details
Study Description
Brief Summary
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
Condition or Disease | Intervention/Treatment | Phase |
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Study Design
Arms and Interventions
Arm | Intervention/Treatment |
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Artificial Intelligence support decision group 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
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.
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Outcome Measures
Primary Outcome Measures
- The diagnostic accuracy of gastric diseases with deep learning algorithm [12 month]
The diagnostic accuracy of gastric diseases with deep learning algorithm
- The accuracy of recommentions for different disease with deep learning algorithm [12 month]
The accuracy of recommentions for different disease with deep learning algorithm
Secondary Outcome Measures
- The diagnostic sensitivity of gastric diseases with deep learning algorithm [12 month]
The diagnostic sensitivity of gastric diseases with deep learning algorithm
- The diagnostic specificity of gastric diseases with deep learning algorithm [12 month]
The diagnostic specificity of gastric diseases with deep learning algorithm
- The diagnostic positive predictive value of gastric diseases with deep learning algorithm [12 month]
The diagnostic positive predictive valu of gastric diseases with deep learning algorithm
- The diagnostic negative predictive value of gastric diseases with deep learning algorithm [12 month]
The diagnostic negative predictive value of gastric diseases with deep learning algorithm
- The F-score of gastric diseases with deep learning algorithm [12 month]
The F-score of gastric diseases with deep learning algorithm
Eligibility Criteria
Criteria
Inclusion Criteria:
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Patients aged 18 - 80 years
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Patients underwent endoscopic examination
Exclusion Criteria:
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Patients with the contraindications to endoscopic examination
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Patients with imcomplete examination information
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Patients undergo endoscopy for therapy
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Patients have history of upper gastrointestinal surgery
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Patients with duodenal or Laryngeal neoplasms
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Patients with gastrointestinal submucosal tumor
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | Qilu Hospital, Shandong University | Jinan | Shandong | China | 250012 |
Sponsors and Collaborators
- Xiuli Zuo
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 2022-SDU-QILU-G008