Artificial Intelligence for Determination of Gastroscopy Surveillance Intervals

Active, not recruiting

• Conditions: Intestinal Metaplasia; High Grade Intraepithelial Neoplasia; Atrophic Gastritis; Early Gastric Cancer; Helicobacter Pylori Infection; Low Grade Intraepithelial Neoplasia; Gastric Cancer
• Interventions: Other: AI recongnize disease and generate recommendations

• Registration Number: NCT05631015
• Lead Sponsor: Xiuli Zuo

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

Detailed Description

Not available

Study Timeline

• Study Start: 2012-01-01
• Primary Completion: 2022-10-31
• Status Verified: 2022-11
• Study First Submitted: 2022-11-19
• Study First Submitted QC: 2022-11-19
• Last Update Submitted: 2022-11-19
• Study First Posted: 2022-11-30
• Last Update Posted: 2022-11-30
• Study Completion: 2023-12-31

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 2000

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Artificial Intelligence support decision group	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.

Primary Outcome Measures

Name	Time	Method
The accuracy of recommentions for different disease with deep learning algorithm	12 month	The accuracy of recommentions for different disease with deep learning algorithm
The diagnostic accuracy of gastric diseases with deep learning algorithm	12 month	The diagnostic accuracy of gastric diseases with deep learning algorithm

Secondary Outcome Measures

Name	Time	Method
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 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 F-score of gastric diseases with deep learning algorithm	12 month	The F-score 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

Trial Locations

Locations (1)

Qilu Hospital, Shandong University; 🇨🇳 Jinan, Shandong, China