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Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases Depending on Tongue Images

Sponsor
Shandong University (Other)
Overall Status
Enrolling by invitation
CT.gov ID
NCT04811599
Collaborator
(none)
2,000
Enrollment
1
Location
14.4
Anticipated Duration (Months)
139.3
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

The purpose of this study is to analysize the relationship between the characteristics of tongue image and the diagnosis of gastrointestinal diseases , then develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases depending on tongue images, so as to improve the objectiveness and intelligence of tongue diagnosis. At the same time, gastrointestinal flora of common tongue images were analyzed in order to provide a microecological basis for understanding the relationship between tongue images and digestive tract diseases.

Condition or Disease Intervention/Treatment Phase

    Detailed Description

    Tongue diagnosis is an important part of traditional Chinese medicine.According to traditional Chinese medicine theory,health condition can assessed by observing tougue features,including color, gloss, shape and coating of the tongue, tongue features reflect gastric mucosal state, disease classification and prognosis. Recently, deep learning based on central neural networks (CNN) has shownTongue diagnosis is an important part of traditional Chinese medicine.According to traditional Chinese medicine theory,health condition can assessed by observing tougue features,including color, gloss, shape and coating of the tongue, tongue features reflect gastric mucosal state, disease classification and prognosis. Recently, deep learning based on central neural networks (CNN) has shown multiple potential in detecting and diagnosing gastrointestinal diseases. However, there is still a blank in recognition of gastrointestinal diseases .This study aims to develop and validate a deep learning algorithm for the diagnosis of digestive tract diseases depending on tongue images,and analyze gastrointestinal flora of common tongue images.

    Study Design

    Study Type:
    Observational
    Anticipated Enrollment :
    2000 participants
    Observational Model:
    Other
    Time Perspective:
    Prospective
    Official Title:
    Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases Depending on Tongue Images
    Actual Study Start Date :
    Mar 21, 2021
    Anticipated Primary Completion Date :
    Jun 1, 2022
    Anticipated Study Completion Date :
    Jun 1, 2022

    Arms and Interventions

    Arm Intervention/Treatment
    deep learning algorithm group

    Before patients going through colonoscopy or gastroscopy ,taking them tongue images and collecting basic information by mobile phone with Anymed.After examination,endoscopic report and histology analysis is collected .Categorizing the images by gastrointestinal diseases，developing and validating a deep learning algorithm for the diagnosis of digestive tract diseases depending on tongue images.Extracting tougue coating,gastric mucosa and stool DNA by high-throughput sequencing,and analyzing their composation,adundance and diversity.

    Outcome Measures

    Primary Outcome Measures

    1. The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm [1 month]

      The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.

    Secondary Outcome Measures

    1. The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm [1 month]

      The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.

    2. The diagnostic specificity of gastrointestinal diseases with deep learning algorithm [1 month]

      The diagnostic specificity of gastrointestinal diseases with deep learning algorithm

    3. The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm [1 month]

      The diagnostic specificity of gastrointestinal diseases with deep learning algorithm

    4. The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm [1 month]

      The diagnostic specificity of gastrointestinal diseases with deep learning algorithm

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years to 80 Years
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    Yes
    Inclusion Criteria:
    • Patients aged 18 - 80 years undergoing endoscopic examination;patients gave informed consent and signed informed consent.
    Exclusion Criteria:

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Qilu Hospital, Shandong University Jinan Shandong China 250012

    Sponsors and Collaborators

    • Shandong University

    Investigators

    • Study Chair: Xiuli Zuo, MD,PhD, Study Principal investigator

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Xiuli Zuo, Director of Qilu Hospital gastroenterology department, Shandong University
    ClinicalTrials.gov Identifier:
    NCT04811599
    Other Study ID Numbers:
    • 2020-SDU-QILU-G056
    First Posted:
    Mar 23, 2021
    Last Update Posted:
    Mar 23, 2021
    Last Verified:
    Mar 1, 2021
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Xiuli Zuo, Director of Qilu Hospital gastroenterology department, Shandong University
    Gastrointestinal Disease
    tongue image
    artificial intelligence
    deep learning
    gastrointestinal flora
    Additional relevant MeSH terms:
    Gastrointestinal Diseases
    Digestive System Diseases

    Study Results

    No Results Posted as of Mar 23, 2021