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

March 20, 2021 updated by: Xiuli Zuo, Shandong University
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.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Unknown

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

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 Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Anticipated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
2000

Contacts and Locations

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

Study Locations

  • China
    • Shandong
      • Jinan, Shandong, China, 250012
        • Qilu Hospital, Shandong University

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

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

18 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients aged 18 - 80 years undergoing endoscopic examination

Description

Inclusion Criteria:

  • Patients aged 18 - 80 years undergoing endoscopic examination;patients gave informed consent and signed informed consent.

Exclusion Criteria:

-

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

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
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.

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm
Time Frame: 1 month
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
1 month

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm
Time Frame: 1 month
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
1 month
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
Time Frame: 1 month
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
1 month
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm
Time Frame: 1 month
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
1 month
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm
Time Frame: 1 month
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
1 month

Collaborators and Investigators

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

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Shandong University

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • Study Chair: Xiuli Zuo, MD,PhD, Study Principal Investigator

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)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
March 21, 2021

Primary Completion (Anticipated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
June 1, 2022

Study Completion (Anticipated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
June 1, 2022

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
March 20, 2021

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
March 20, 2021

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
March 23, 2021

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
March 23, 2021

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
March 20, 2021

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
March 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • 2020-SDU-QILU-G056

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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