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Application of Artificial Intelligence on the Diagnosis of Helicobacter Pylori Infection and Premalignant Gastric Lesion

Sponsor
National Taiwan University Hospital (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT05762991
Collaborator
(none)
2,000
Enrollment
46.1
Anticipated Duration (Months)

Study Details

Study Description

Brief Summary

The aim of this observational study is to learn the application of artificial intelligence on the diagnosis of Helicobacter pylori infection and premalignant gastric lesion. We use convolutional neural networks of artificial intelligence to analyze the correlation between endoscopic images and urea breath test results/histopathological results.

Condition or Disease Intervention/Treatment Phase

    Detailed Description

    This study will invite patients who need to undergo urea breath test, upper gastrointestinal endoscopy and histology examination, and collect their tests results, upper gastrointestinal endoscopy images and histopathological results. We use convolutional neural networks of artificial intelligence to analyze the correlation between endoscopic images and urea breath test results/histopathological results. We hope that this analysis system can be established to assist clinicians in diagnosing Helicobacter pylori infection and detecting premalignant gastric lesion by endoscopic examination.

    Study Design

    Study Type:
    Observational
    Anticipated Enrollment :
    2000 participants
    Observational Model:
    Cohort
    Time Perspective:
    Prospective
    Official Title:
    Application of Artificial Intelligence on the Diagnosis of Helicobacter Pylori Infection and Premalignant Gastric Lesion: A Multihospital Study
    Anticipated Study Start Date :
    Feb 28, 2023
    Anticipated Primary Completion Date :
    Dec 31, 2026
    Anticipated Study Completion Date :
    Dec 31, 2026

    Arms and Interventions

    Arm Intervention/Treatment
    Helicobacter pylori infection and premalignant gastric lesion

    Application of artificial intelligence to analyze the correlation between endoscopic images and urea breath test results/histopathological results.

    Outcome Measures

    Primary Outcome Measures

    1. Application of artificial intelligence on the diagnosis of Helicobacter pylori infection and premalignant gastric lesion [4 years]

      We use convolutional neural networks of artificial intelligence to analyze the correlation between endoscopic images and urea breath test results/histopathological results.

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    20 Years to 80 Years
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    Inclusion Criteria:

    1. Age 20-80

    2. Scheduled urea breath test and endoscopy

    Exclusion Criteria:
    1. History of gastric surgery

    Contacts and Locations

    Locations

    No locations specified.

    Sponsors and Collaborators

    • National Taiwan University Hospital

    Investigators

    • Principal Investigator: Tsung-Hsien Chiang, MD, PhD, National Taiwan University Hospital

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    National Taiwan University Hospital
    ClinicalTrials.gov Identifier:
    NCT05762991
    Other Study ID Numbers:
    • 202111108RINC
    First Posted:
    Mar 10, 2023
    Last Update Posted:
    Mar 10, 2023
    Last Verified:
    Feb 1, 2023
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Additional relevant MeSH terms:
    Infections
    Communicable Diseases
    Helicobacter Infections
    Precancerous Conditions

    Study Results

    No Results Posted as of Mar 10, 2023