Artificial Intelligence for Determination of Gastroscopy Surveillance Intervals

Sponsor
Xiuli Zuo (Other)
Overall Status
Active, not recruiting
CT.gov ID
NCT05631015
Collaborator
(none)
2,000
1
144
13.9

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
  • Other: AI recongnize disease and generate recommendations

Study Design

Study Type:
Observational
Anticipated Enrollment :
2000 participants
Observational Model:
Other
Time Perspective:
Retrospective
Official Title:
Development and Validation of Gastroscopy Surveillance Recommendations Based on Natural Language Processing for Patients With Gastric Cancer and Precancerous Diseases
Actual Study Start Date :
Jan 1, 2012
Actual Primary Completion Date :
Oct 31, 2022
Anticipated Study Completion Date :
Dec 31, 2023

Arms and Interventions

Arm Intervention/Treatment
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.

Outcome Measures

Primary Outcome Measures

  1. The diagnostic accuracy of gastric diseases with deep learning algorithm [12 month]

    The diagnostic accuracy of gastric diseases with deep learning algorithm

  2. 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

  1. The diagnostic sensitivity of gastric diseases with deep learning algorithm [12 month]

    The diagnostic sensitivity of gastric diseases with deep learning algorithm

  2. The diagnostic specificity of gastric diseases with deep learning algorithm [12 month]

    The diagnostic specificity of gastric diseases with deep learning algorithm

  3. 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

  4. 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

  5. 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

Ages Eligible for Study:
18 Years to 80 Years
Sexes Eligible for Study:
All
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

Contacts and Locations

Locations

Site City State Country Postal Code
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.
Responsible Party:
Xiuli Zuo, director of Qilu Hospital gastroenterology department, Shandong University
ClinicalTrials.gov Identifier:
NCT05631015
Other Study ID Numbers:
  • 2022-SDU-QILU-G008
First Posted:
Nov 30, 2022
Last Update Posted:
Nov 30, 2022
Last Verified:
Nov 1, 2022
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Additional relevant MeSH terms:

Study Results

No Results Posted as of Nov 30, 2022