Gastrointestinal DiseaseNCT04811599
Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases Depending on Tongue Images
An observational study of Gastrointestinal Disease, run by Shandong University at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Gastrointestinal Disease
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 21 Mar 2021to 1 Jun 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients aged 18 - 80 years undergoing endoscopic examination
Who may be able to join
- Patients aged 18 - 80 years undergoing endoscopic examination;patients gave informed consent and signed informed consent.
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the 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.
- Conditions
- Gastrointestinal Disease
- Keywords
- Gastrointestinal Disease, tongue image, artificial intelligence, deep learning, gastrointestinal flora
- Medical subject headings
- Gastrointestinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
Measured: 1 month
Secondary outcomes (4)
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
Measured: 1 month
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
Measured: 1 month
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
Measured: 1 month
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Mar 2021
- Study start21 Mar 2021
- Primary completion1 Jun 2022 (estimated)
- Study completion1 Jun 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastrointestinal DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiuli Zuo, Shandong University
- Investigators
- Xiuli Zuo, MD,PhD, Study chair, Study Principal investigator
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04811599
- Sponsor's protocol ID
- 2020-SDU-QILU-G056
- Status last verified
- Mar 2021
- First submitted
- 20 Mar 2021
- Last updated
- 23 Mar 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Mar 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases Depending on Tongue Images
- ClinicalTrials.gov ID
- NCT04811599
- Acronym / sponsor ID
- 2020-SDU-QILU-G056
- Conditions
- Gastrointestinal Disease
- Keywords
- Gastrointestinal Disease, tongue image, artificial intelligence, deep learning, gastrointestinal flora
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 21 Mar 2021 (actual)
- Primary completion
- 1 Jun 2022 (estimated)
- Study completion
- 1 Jun 2022 (estimated)
- First posted
- 23 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients aged 18 - 80 years undergoing endoscopic examination
Key inclusion criteria (1 listed)
- Patients aged 18 - 80 years undergoing endoscopic examination;patients gave informed consent and signed informed consent.
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm
Time frame: 1 month
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
4 secondary outcomes
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm
Time frame: 1 month
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm
Time frame: 1 month
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm
Time frame: 1 month
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm
Time frame: 1 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Mar 2021.
Related trials for Gastrointestinal Disease
Active, not recruitingObservational study
A Study to Learn About the Effectiveness of Etrasimod in People With Ulcerative Colitis
Colitis, Ulcerative
Active, not recruitingPhase 1/2
A Beta-only IL-2 ImmunoTherapY Study
Advanced Solid Tumor, Unresectable Solid Tumor
RecruitingPhase 1
A Study of MGC030 in Participants With Advanced Solid Tumors
Urothelial Cancer, GastroEsophageal Cancer
Active, not recruitingPhase 3
Efficacy and Safety of Tezepelumab in Patients With Eosinophilic Esophagitis
Eosinophilic Esophagitis
Not yet recruitingObservational study
A Study of Fatigue, Itching and Daily Activities in People With Primary Biliary Cholangitis Starting Elafibranor
Primary Biliary Cholangitis (PBC)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Xiuli Zuo, MD,PhD study chair
Study Principal investigator
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastrointestinal Disease, run by Shandong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 23 Mar 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm (time frame: 1 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Mar 2021.