Artificial IntelligenceNCT05369572
Connection Between Tongue Signs and Bile Reflux Analysed With Artificial Intelligence
An observational study of Artificial Intelligence and Tongue, run by Shandong University at 1 site in China.
Trial summary
By introducing artificial intelligence into Chinese medicine tongue diagnosis, we collated and collected tongue images, anxiety and depression scales and gastroscopy reports, mined and analysed the correlation between tongue images and bile reflux and anxiety and depression and constructed a prediction model to… Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Tongue, Bile Reflux
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 30 Jun 2022to 30 Jun 2025
- 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 who will undergo gastroscopy and who fulfil the inclusion criteria and do not fulfil the exclusion criteria.
Who may be able to join
- Patients aged 18 to 80 years who wish to undergo gastroscopy.
- Patients have given their informed consent and signed the informed consent form.
Who cannot join
- Serious heart, liver, kidney or other underlying illness, or mental illness.
- Patients taking anti-anxiety or depression medication within 3 months.
- Current H. pylori infection.
- History of surgery on the digestive or biliary tract.
- Peptic ulcer, malignant tumour of the digestive tract, etc.
- Patients taking bismuth or other staining medications.
- Pregnant or lactating women.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Analysing the Link Between Tongue Signs and Bile Reflux by Artificial Intelligence
By introducing artificial intelligence into Chinese medicine tongue diagnosis, we collated and collected tongue images, anxiety and depression scales and gastroscopy reports, mined and analysed the correlation between tongue images and bile reflux and anxiety and depression and constructed a prediction model to analyse the possibility of predicting bile reflux and anxiety and depression in patients based on tongue images.
Read the detailed description
Firstly, after the patient signs the informed consent form, the researcher will collect pictures of the patient's tongue and obtain basic information about the patient.
Second, the patients are scored on the Anxiety and Depression Scale.
Thirdly, after the patient undergoes gastroscopy, the patient's gastroscopy report is obtained.
Finally, the patient's tongue image, information and gastroscopy report are matched to construct an artificial intelligence model of tongue image and bile reflux and anxiety and depression, and the quality of the model is assessed.
- Conditions
- Artificial Intelligence, Tongue, Bile Reflux
- Keywords
- artificial intelligence, tongue, bile reflux
- Medical subject headings
- Bile Reflux
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 |
|---|---|---|
| Bile Reflux Group | Gastroscopic reports of enrolled patients will be extracted and patients will be identified as having bile reflux according to Kellosalo J classification. Grade I: small amount of yellowish reflux emerging from the pyloric orifice and/or yellowish staining of the mucus lake, which is pale yellow in colour. Grade II: intermittent gush of reflux from the pyloric opening and/or yellowish staining of the mucus lake, which is dark yellow. Grade III: frequent gush of yellow-green reflux from the pyloric orifice and/or yellow-green mucus covering the stomach. | |
| Non-biliary reflux group | Gastroscopic reports will be extracted from patients enrolled in the group that do not meet the Kellosalo J classification as the non-biliary reflux group. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
Sensitivity of artificial intelligence models Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%.
Measured: 3 years
Specificity
Specificity of Artificial Intelligence Models Specificity = number of true negatives / (number of true negatives + number of false positives))
*100%
Measured: 3 years
Positive predictive values(PPV)
Positive predictive values from artificial intelligence models Positive predictive value = true positive / (true positive + false positive) *100%
Measured: 3 years
Negative predictive values (NPV)
Negative predictive values for artificial intelligence models Negative Predictive Value = True Negative / (True Negative + False Negative) *100%
Measured: 3 years
AUC (95% CI)
area under the receiver operating characteristic curve (AUC),
Measured: 3 years
Accuracy
Accuracy for artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects * 100%
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 May 2022
- Study start30 Jun 2022 (estimated)
- Primary completion30 Jun 2024 (estimated)
- Study completion30 Jun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,500 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, Principal investigator, Qilu Hospital of Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05369572
- Sponsor's protocol ID
- 2022SDU-QILU-G002
- Status last verified
- Jun 2022
- First submitted
- 5 May 2022
- Last updated
- 22 Jun 2022
- 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 22 Jun 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Analysing the Link Between Tongue Signs and Bile Reflux by Artificial Intelligence
- ClinicalTrials.gov ID
- NCT05369572
- Acronym / sponsor ID
- 2022SDU-QILU-G002
- Conditions
- Artificial Intelligence, Tongue, Bile Reflux
- Keywords
- artificial intelligence, tongue, bile reflux
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 30 Jun 2022 (estimated)
- Primary completion
- 30 Jun 2024 (estimated)
- Study completion
- 30 Jun 2025 (estimated)
- First posted
- 11 May 2022 (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.
By introducing artificial intelligence into Chinese medicine tongue diagnosis, we collated and collected tongue images, anxiety and depression scales and gastroscopy reports, mined and analysed the correlation between tongue images and bile reflux and anxiety and depression and constructed a prediction model to analyse the possibility of predicting bile reflux and anxiety and depression in patients based on tongue images.
Detailed description
Firstly, after the patient signs the informed consent form, the researcher will collect pictures of the patient's tongue and obtain basic information about the patient.
Second, the patients are scored on the Anxiety and Depression Scale.
Thirdly, after the patient undergoes gastroscopy, the patient's gastroscopy report is obtained.
Finally, the patient's tongue image, information and gastroscopy report are matched to construct an artificial intelligence model of tongue image and bile reflux and anxiety and depression, and the quality of the model is assessed.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Bile Reflux Group | – | –Gastroscopic reports of enrolled patients will be extracted and patients will be identified as having bile reflux according to Kellosalo J classification. Grade I: small amount of yellowish reflux emerging from the pyloric orifice and/or yellowish staining of the mucus lake, which is pale yellow in colour. Grade II: intermittent gush of reflux from the pyloric opening and/or yellowish staining of the mucus lake, which is dark yellow. Grade III: frequent gush of yellow-green reflux from the pyloric orifice and/or yellow-green mucus covering the stomach. |
| Non-biliary reflux group | – | –Gastroscopic reports will be extracted from patients enrolled in the group that do not meet the Kellosalo J classification as the non-biliary reflux group. |
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 who will undergo gastroscopy and who fulfil the inclusion criteria and do not fulfil the exclusion criteria.
Key inclusion criteria (2 listed)
- Patients aged 18 to 80 years who wish to undergo gastroscopy.
- Patients have given their informed consent and signed the informed consent form.
Key exclusion criteria (7 listed)
- Serious heart, liver, kidney or other underlying illness, or mental illness.
- Patients taking anti-anxiety or depression medication within 3 months.
- Current H. pylori infection.
- History of surgery on the digestive or biliary tract.
- Peptic ulcer, malignant tumour of the digestive tract, etc.
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
Sensitivity
Time frame: 3 years
Sensitivity of artificial intelligence models Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%.
Specificity
Time frame: 3 years
Specificity of Artificial Intelligence Models Specificity = number of true negatives / (number of true negatives + number of false positives)) *100%
Positive predictive values(PPV)
Time frame: 3 years
Positive predictive values from artificial intelligence models Positive predictive value = true positive / (true positive + false positive) *100%
Negative predictive values (NPV)
Time frame: 3 years
Negative predictive values for artificial intelligence models Negative Predictive Value = True Negative / (True Negative + False Negative) *100%
AUC (95% CI)
Time frame: 3 years
area under the receiver operating characteristic curve (AUC),
Accuracy
Time frame: 3 years
Accuracy for artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects * 100%
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jun 2022.
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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 principal investigator
Qilu Hospital of Shandong University
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 Artificial Intelligence and Tongue, 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 2 inclusion and 7 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 22 Jun 2022. 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?
Contact Xiuli Zuo, MD, PhD (86 15588818685 ext. 0531-88369277, zuoxiuli@sdu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: 3 years). The study lists 6 primary outcomes in all. 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 22 Jun 2022.