AI in GIM Diagnosis

March 28, 2022 updated by: Rapat Pittayanon, King Chulalongkorn Memorial Hospital

Usefulness of Artificial Intelligence (AI) for Gastric Intestinal Metaplasia Diagnosis

This study will use artificial intelligence (AI) for diagnosing gastric intestinal metaplasia.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

The patients with previously diagnose gastric intestinal metaplasia (GIM) and have the surveillance gastroscopy will be enrolled. The routine surveillance program will be performed additional to taking photo at both GIM and normal mucosa at least 5 pictures in each. Biopsy will be done to confirm the diagnosis of GIM and normal mucosa. All pictures will be inserted to AI algorithm based on the convolutional neural network (CNN). Then, the AI program will be validated in daily endoscopy compared with pathology. Accuracy, sensitivity and specificity can be calculated by 2x2 table.

Study Type

Interventional

Enrollment (Anticipated)

120

Phase

  • Not Applicable

Contacts and Locations

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

Study Contact

Study Locations

  • Thailand
    • Bangkok
      • Pathum Wan, Bangkok, Thailand, 10330

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

14 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • More than 18 years of age
  • Able to sign a consent form

Exclusion Criteria:

  • History of gastric surgery
  • Coagulopathy
  • Pregnancy/Breast feeding

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

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: GIM patient
The patients with GIM will be assessed at both GIM and normal mucosa during endoscopy.
Diagnostic test: Artificial intelligence
The AI algorithm based on the convolutional neural network (CNN) will be used for analysis the pictures of gastric intestinal metaplasia and normal mucosa. Then AI will be used as a diagnostic tool for GIM during routine endoscopy by using pathology as a gold standard.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of AI for GIM diagnosis
Time Frame: 1 year
Accuracy, sensitivity, specificity can be calculated by 2x2 table (pathology is a gold standard)
1 year

Collaborators and Investigators

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

Sponsor

King Chulalongkorn Memorial Hospital

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)

May 1, 2020

Primary Completion (Anticipated)

November 30, 2023

Study Completion (Anticipated)

February 28, 2024

Study Registration Dates

First Submitted

April 12, 2020

First Submitted That Met QC Criteria

April 21, 2020

First Posted (Actual)

April 24, 2020

Study Record Updates

Last Update Posted (Actual)

March 31, 2022

Last Update Submitted That Met QC Criteria

March 28, 2022

Last Verified

March 1, 2022

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • RP018

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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