Artificial Intelligence for Determination of Gastroscopy Surveillance Intervals

NCT05631015 | Unknown

• 1 other identifier: 2022-SDU-QILU-G008
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

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

Conditions

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

Outcome Measures

Primary Outcomes (2)

• 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 Outcomes (5)

• The diagnostic sensitivity of gastric diseases with deep learning algorithm

  12 month

• The diagnostic specificity of gastric diseases with deep learning algorithm

  12 month

• The diagnostic positive predictive value of gastric diseases with deep learning algorithm

  12 month

• The diagnostic negative predictive value of gastric diseases with deep learning algorithm

  12 month

• The F-score of gastric diseases with deep learning algorithm

  12 month

Study Arms (1)

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

Interventions

AI recongnize disease and generate recommendations OTHER

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.

Artificial Intelligence support decision group

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients who came to Qilu Hospital of Shandong University and received endoscopy examination but not therapeutic endoscopy

You may qualify if:

• Patients aged 18 - 80 years
• Patients underwent endoscopic examination

You may not qualify if:

• 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

Sponsors & Collaborators

• Xiuli Zuo: lead

Study Sites (1)

Qilu Hospital, Shandong University

Jinan, Shandong, 250012, China

Location

MeSH Terms

Conditions

Gastritis, Atrophic; Stomach Neoplasms

Condition Hierarchy (Ancestors)

Gastritis; Gastroenteritis; Gastrointestinal Diseases; Digestive System Diseases; Stomach Diseases; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: director of Qilu Hospital gastroenterology department

Study Record Dates

• First Submitted: November 19, 2022
• First Posted: November 30, 2022
• Study Start: January 1, 2012
• Primary Completion: October 31, 2022
• Study Completion: December 31, 2023
• Last Updated: November 30, 2022

Record last verified: 2022-11

Locations

CN (1)