Artificial Intelligence for Determination of Gastroscopy Surveillance Intervals

November 19, 2022 updated by: Xiuli Zuo

Development and Validation of Gastroscopy Surveillance Recommendations Based on Natural Language Processing for Patients With Gastric Cancer and Precancerous Diseases

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

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Anticipated)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Shandong
      • Jinan, Shandong, China, 250012
        • Qilu Hospital, Shandong University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

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

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Other
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The diagnostic accuracy of gastric diseases with deep learning algorithm
Time Frame: 12 month
The diagnostic accuracy of gastric diseases with deep learning algorithm
12 month
The accuracy of recommentions for different disease with deep learning algorithm
Time Frame: 12 month
The accuracy of recommentions for different disease with deep learning algorithm
12 month

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The diagnostic sensitivity of gastric diseases with deep learning algorithm
Time Frame: 12 month
The diagnostic sensitivity of gastric diseases with deep learning algorithm
12 month
The diagnostic specificity of gastric diseases with deep learning algorithm
Time Frame: 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
Time Frame: 12 month
The diagnostic positive predictive valu of gastric diseases with deep learning algorithm
12 month
The diagnostic negative predictive value of gastric diseases with deep learning algorithm
Time Frame: 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
Time Frame: 12 month
The F-score of gastric diseases with deep learning algorithm
12 month

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Xiuli Zuo

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2012

Primary Completion (Actual)

October 31, 2022

Study Completion (Anticipated)

December 31, 2023

Study Registration Dates

First Submitted

November 19, 2022

First Submitted That Met QC Criteria

November 19, 2022

First Posted (Actual)

November 30, 2022

Study Record Updates

Last Update Posted (Actual)

November 30, 2022

Last Update Submitted That Met QC Criteria

November 19, 2022

Last Verified

November 1, 2022

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2022-SDU-QILU-G008

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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