AI in GIM Diagnosis

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

Status

UNKNOWN

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT04358198

Enrollment

120

Registered

2020-04-24

Start date

2020-05-01

Completion date

2024-02-28

Last updated

2022-03-31

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intellegence, GIM Diagnosis

Brief summary

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

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.

Interventions

DIAGNOSTIC_TESTArtificial 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.

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Intervention model description

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

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

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

Exclusion criteria

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

Design outcomes

Primary

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

Countries

Thailand

Contacts

Primary ContactRapat Pittayanon, MD

rapat125@gmail.com66804224999

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026