NCT04358198

Brief Summary

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

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
120

participants targeted

Target at P50-P75 for not_applicable

Timeline
Completed

Started May 2020

Longer than P75 for not_applicable

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

April 12, 2020

Completed
12 days until next milestone

First Posted

Study publicly available on registry

April 24, 2020

Completed
7 days until next milestone

Study Start

First participant enrolled

May 1, 2020

Completed
3.6 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

November 30, 2023

Completed
3 months until next milestone

Study Completion

Last participant's last visit for all outcomes

February 28, 2024

Completed
Last Updated

March 31, 2022

Status Verified

March 1, 2022

Enrollment Period

3.6 years

First QC Date

April 12, 2020

Last Update Submit

March 28, 2022

Conditions

GIM DiagnosisArtificial Intellegence

Outcome Measures

Primary Outcomes (1)

  • Accuracy of AI for GIM diagnosis

    Accuracy, sensitivity, specificity can be calculated by 2x2 table (pathology is a gold standard)

    1 year

Study Arms (1)

GIM patient

EXPERIMENTAL

The patients with GIM will be assessed at both GIM and normal mucosa during endoscopy.

Diagnostic Test: Artificial intelligence

Interventions

Artificial intelligenceDIAGNOSTIC_TEST

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.

GIM patient

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Rapat Pittayanon

Pathum Wan, Bangkok, 10330, Thailand

RECRUITING

MeSH Terms

Interventions

Artificial Intelligence

Intervention Hierarchy (Ancestors)

AlgorithmsMathematical Concepts

Central Study Contacts

Rapat Pittayanon, MD

CONTACT

66804224999rapat125@gmail.com

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Purpose
DIAGNOSTIC
Intervention Model
SINGLE GROUP
Model Details: The surveillance EGD in patients with GIM will be done as scheduled and then pictures at GIM lesions and normal mucosa was done and sending to AI for learning. Then AI will be used for diagnosing GIM by using pathology as a gold standard
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Principle investigator

Study Record Dates

First Submitted

April 12, 2020

First Posted

April 24, 2020

Study Start

May 1, 2020

Primary Completion

November 30, 2023

Study Completion

February 28, 2024

Last Updated

March 31, 2022

Record last verified: 2022-03

Locations

TH(1)