An observational study in Helicobacter Pylori Infection, Atrophic Gastritis and Intestinal Metaplasia, sponsored by Xiuli Zuo. Status unknown at 1 site in China. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2022-11-30.
Sponsored by Xiuli Zuo · Observational
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
2,851 studies on the registry are indexed under Stomach Neoplasms; 864 are open to participants now.
This study's planned enrollment of 2,000 is above the median of 274 across 670 observational studies indexed under Stomach Neoplasms.
Browse Stomach Neoplasms studies →Xiuli Zuo is the lead sponsor of 10 studies on the registry; 5 are open to participants now.
Counted across the registry records on this site, refreshed daily.
patients who came to Qilu Hospital of Shandong University and received endoscopy examination but not therapeutic endoscopy
Exclusion Criteria:
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.
The diagnostic accuracy of gastric diseases with deep learning algorithm
The diagnostic accuracy of gastric diseases with deep learning algorithm
Time frame: 12 month
The accuracy of recommentions for different disease with deep learning algorithm
The accuracy of recommentions for different disease with deep learning algorithm
Time frame: 12 month
The diagnostic sensitivity of gastric diseases with deep learning algorithm
The diagnostic sensitivity of gastric diseases with deep learning algorithm
Time frame: 12 month
The diagnostic specificity of gastric diseases with deep learning algorithm
The diagnostic specificity of gastric diseases with deep learning algorithm
Time frame: 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
Time frame: 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
Time frame: 12 month
The F-score of gastric diseases with deep learning algorithm
The F-score of gastric diseases with deep learning algorithm
Time frame: 12 month
This study is status unknown, as verified in Nov 2022. You cannot join it, but the record below documents what was studied.
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Xiuli Zuo