Medicine, Chinese TraditionalNCT06525025
Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database
An observational study of Medicine, Chinese Traditional and Artificial Intelligence, run by Fifth Affiliated Hospital, Sun Yat-Sen University.
Trial summary
Collecting Traditional Chinese Medicine (TCM) clinical diagnosis and treatment data, including doctor-patient dialogues, tongue diagnosis, facial diagnosis, and TCM constitution information, to construct databases for tongue diagnosis, TCM constitution, and doctor-patient dialogues. Read more about this study
- Who is it for?
- Ages 18 to 85 yearsHealthy volunteers can take part
- Conditions the study is for
- Medicine, Chinese Traditional, Artificial Intelligence
- What is being tested?
- Observational study, non intervention
- How many people
- 80,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Aug 2024to 15 Aug 2026
- 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 85 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: There are no specific restrictions on the disease, gender, or health status of the enrolled population.
Who may be able to join
- People who come to the hospital for physical examination and medical treatment；
- Participants voluntarily participate in the study.
Who cannot join
- Subjects with difficulty in tongue extension, communication, etc. who cannot cooperate with data collection；
- The researchers determined that there were other factors that may have forced the subjects to terminate the study.
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database: A Prospective Multicenter Cross-Sectional Study
Collecting Traditional Chinese Medicine (TCM) clinical diagnosis and treatment data, including doctor-patient dialogues, tongue diagnosis, facial diagnosis, and TCM constitution information, to construct databases for tongue diagnosis, TCM constitution, and doctor-patient dialogues. Based on artificial intelligence technology, engage in research related to the standardization and intelligentization of TCM.
Read the detailed description
The technological principles of large language models align with the empirical medical principles of Traditional Chinese Medicine (TCM), and the rise of large model technology can greatly promote the progress of TCM. However, there is currently a lack of clinical diagnosis and treatment databases with TCM characteristics for training TCM artificial intelligence(AI) large models.
At present, a large-scale tongue image database has not yet been established for modeling common TCM tongue appearances, thereby ensuring the accuracy and consistency of TCM diagnosis and promoting the objective standardization of TCM diagnostic development.
Considering the feedback from the subjects in clinical work that the TCM constitution survey questionnaire has a large volume, takes a long time, and has certain subjective issues, we plan to carry out a large-scale clinical observational study to optimize the process of TCM constitution identification.
Traditional Chinese Medicine (TCM) doctor-patient dialogues and medical record writing are essential entities generated during the TCM diagnosis and treatment process. Assisting in consultation, medical record generation, and treatment plan recommendations based on doctor-patient dialogues have significant clinical and research value. Therefore, we plan to collect a large number of doctor-patient dialogues and outpatient medical records to construct a doctor-patient dialogue database, preparing in advance for optimizing interactive large-scale TCM models.
In summary, the research on constructing a TCM clinical diagnosis and treatment database has important clinical and scientific research value. This will help to improve the standardization and normalization of TCM diagnosis and treatment, and also support the modernization and internationalization of TCM. By applying big data analysis and artificial intelligence technology, it is possible to delve deeper into TCM diagnosis and treatment information, providing richer and more accurate data resources for clinical decision-making and scientific research exploration in TCM.
- Being tested
- Observational study, non intervention
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Observational study, non interventionOther
Observational study, non intervention
| Study group | Role | What they receive |
|---|---|---|
| Traditional Chinese Medicine Tongue Image Group | Internally, using random allocation, divided into training group and validation group Receives: Other: Observational study, non intervention | |
| Traditional Chinese Medicine Constitution Data Group | Internally, using random allocation, divided into training group and validation group Receives: Other: Observational study, non intervention | |
| Traditional Chinese Medicine Doctor Patient Dialogue Data Group | Data used for fine-tuning traditional Chinese medicine models Receives: Other: Observational study, non intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Development of a tongue image-based machine learning tool
- It is anticipated to enroll 50,000 samples to establish a Traditional Chinese Medicine (TCM) tongue appearance database.
- The tongue images will undergo quality selection and preprocessing.
- The tongue images will be manually annotated, with 40% allocated to the training group and 60% to the testing group.
- For the training group: A TCM tongue appearance model will be constructed based on the manually annotated tongue images.
- For the testing group: The TCM tongue appearance model will be used to interpret the tongue images.
- Analyze the consistency between the tongue appearance interpretations by the model built from the training group and the readings by TCM physicians for the testing group's tongue images.
Measured: 20 months
Secondary outcomes (1)
TCM Constitution Multimodal Model
- It is projected to include 20,000 samples to construct a Traditional Chinese Medicine (TCM) constitution database.
- Of these, 15,000 samples will complete the TCM constitution survey questionnaire and have their tongue and facial diagnosis data collected.
- Based on the data from step 2, a multimodal TCM constitution assessment model will be constructed.
- An additional 5,000 samples will be tested, first by completing the multimodal TCM constitution assessment model, and then the TCM constitution survey questionnaire.
- Analyze the consistency between the readings of the TCM constitution assessment model and the results from the TCM constitution survey questionnaire generation, and treatment plan recommendations, prepare in advance for optimizing interactive large-scale TCM models.
Measured: 20 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2024
- Study start1 Aug 2024 (estimated)
- Primary completion15 Aug 2024 (estimated)
- Study completion15 Aug 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Medicine, Chinese TraditionalStudies 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
- 80,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fifth Affiliated Hospital, Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Qi Zeng, Doctor, Principal investigator, Fifth Affiliated Hospital, Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06525025
- Sponsor's protocol ID
- ZDWY.ZYZLK.009
- Status last verified
- Jul 2024
- First submitted
- 18 Jul 2024
- Last updated
- 29 Jul 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Tian F, Liu D, Wei N, Fu Q, Sun L, Liu W, Sui X, Tian K, Nemeth G, Feng J, Xu J, Xiao L, Han J, Fu J, Shi Y, Yang Y, Liu J, Hu C, Feng B, Sun Y, Wang Y, Yu G, Kong D, Wang M, Li W, Chen K, Li X. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning. Nat Med. 2024 May;30(5):1309-1319. doi: 10.1038/s41591-024-02915-w. Epub 2024 Apr 16.
PubMed 38627559Yuan L, Yang L, Zhang S, Xu Z, Qin J, Shi Y, Yu P, Wang Y, Bao Z, Xia Y, Sun J, He W, Chen T, Chen X, Hu C, Zhang Y, Dong C, Zhao P, Wang Y, Jiang N, Lv B, Xue Y, Jiao B, Gao H, Chai K, Li J, Wang H, Wang X, Guan X, Liu X, Zhao G, Zheng Z, Yan J, Yu H, Chen L, Ye Z, You H, Bao Y, Cheng X, Zhao P, Wang L, Zeng W, Tian Y, Chen M, You Y, Yuan G, Ruan H, Gao X, Xu J, Xu H, Du L, Zhang S, Fu H, Cheng X. Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study. EClinicalMedicine. 2023 Feb 6;57:101834. doi: 10.1016/j.eclinm.2023.101834. eCollection 2023 Mar.
PubMed 36825238Tan Y, Zhang Z, Li M, Pan F, Duan H, Huang Z, Deng H, Yu Z, Yang C, Shen G, Qi P, Yue C, Liu Y, Hong L, Yu H, Fan G, Tang Y. MedChatZH: A tuning LLM for traditional Chinese medicine consultations. Comput Biol Med. 2024 Apr;172:108290. doi: 10.1016/j.compbiomed.2024.108290. Epub 2024 Mar 13.
PubMed 38503097
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database: A Prospective Multicenter Cross-Sectional Study
- ClinicalTrials.gov ID
- NCT06525025
- Acronym / sponsor ID
- ZDWY.ZYZLK.009
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 80,000 participants (estimated)
- Lead sponsor
- Fifth Affiliated Hospital, Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 1 Aug 2024 (estimated)
- Primary completion
- 15 Aug 2024 (estimated)
- Study completion
- 15 Aug 2026 (estimated)
- First posted
- 29 Jul 2024 (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.
Collecting Traditional Chinese Medicine (TCM) clinical diagnosis and treatment data, including doctor-patient dialogues, tongue diagnosis, facial diagnosis, and TCM constitution information, to construct databases for tongue diagnosis, TCM constitution, and doctor-patient dialogues. Based on artificial intelligence technology, engage in research related to the standardization and intelligentization of TCM.
Detailed description
The technological principles of large language models align with the empirical medical principles of Traditional Chinese Medicine (TCM), and the rise of large model technology can greatly promote the progress of TCM. However, there is currently a lack of clinical diagnosis and treatment databases with TCM characteristics for training TCM artificial intelligence(AI) large models.
At present, a large-scale tongue image database has not yet been established for modeling common TCM tongue appearances, thereby ensuring the accuracy and consistency of TCM diagnosis and promoting the objective standardization of TCM diagnostic development.
Considering the feedback from the subjects in clinical work that the TCM constitution survey questionnaire has a large volume, takes a long time, and has certain subjective issues, we plan to carry out a large-scale clinical observational study to optimize the process of TCM constitution identification.
Traditional Chinese Medicine (TCM) doctor-patient dialogues and medical record writing are essential entities generated during the TCM diagnosis and treatment process. Assisting in consultation, medical record generation, and treatment plan recommendations based on doctor-patient dialogues have significant clinical and research value. Therefore, we plan to collect a large number of doctor-patient dialogues and outpatient medical records to construct a doctor-patient dialogue database, preparing in advance for optimizing interactive large-scale TCM models.
In summary, the research on constructing a TCM clinical diagnosis and treatment database has important clinical and scientific research value. This will help to improve the standardization and normalization of TCM diagnosis and treatment, and also support the modernization and internationalization of TCM. By applying big data analysis and artificial intelligence technology, it is possible to delve deeper into TCM diagnosis and treatment information, providing richer and more accurate data resources for clinical decision-making and scientific research exploration in TCM.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Traditional Chinese Medicine Tongue Image Group | – | Other: Observational study, non interventionInternally, using random allocation, divided into training group and validation group |
| Traditional Chinese Medicine Constitution Data Group | – | Other: Observational study, non interventionInternally, using random allocation, divided into training group and validation group |
| Traditional Chinese Medicine Doctor Patient Dialogue Data Group | – | Other: Observational study, non interventionData used for fine-tuning traditional Chinese medicine models |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- There are no specific restrictions on the disease, gender, or health status of the enrolled population.
Key inclusion criteria (2 listed)
- People who come to the hospital for physical examination and medical treatment；
- Participants voluntarily participate in the study.
Key exclusion criteria (2 listed)
- Subjects with difficulty in tongue extension, communication, etc. who cannot cooperate with data collection；
- The researchers determined that there were other factors that may have forced the subjects to terminate the study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Observational study, non interventionObservational study, non intervention | Other | Traditional Chinese Medicine Constitution Data Group, Traditional Chinese Medicine Doctor Patient Dialogue Data Group, Traditional Chinese Medicine Tongue Image Group |
Primary outcome
Development of a tongue image-based machine learning tool
Time frame: 20 months
1. It is anticipated to enroll 50,000 samples to establish a Traditional Chinese Medicine (TCM) tongue appearance database. 2. The tongue images will undergo quality selection and preprocessing. 3. The tongue images will be manually annotated, with 40% allocated to the training group and 60% to the testing group. 4. For the training group: A TCM tongue appearance model will be constructed based on the manually annotated tongue images. 5. For the testing group: The TCM tongue appearance model will be used to interpret the tongue images. 6. Analyze the consistency between the tongue appearance interpretations by the model built from the training group and the readings by TCM physicians for the testing group's tongue images.
1 secondary outcome
TCM Constitution Multimodal Model
Time frame: 20 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jul 2024.
- Background: PubMed 38627559Tian F, Liu D, Wei N, Fu Q, Sun L, Liu W, Sui X, Tian K, Nemeth G, Feng J, Xu J, Xiao L, Han J, Fu J, Shi Y, Yang Y, Liu J, Hu C, Feng B, Sun Y, Wang Y, Yu G, Kong D, Wang M, Li W, Chen K, Li X. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning. Nat Med. 2024 May;30(5):1309-1319. doi: 10.1038/s41591-024-02915-w. Epub 2024 Apr 16.
- Background: PubMed 36825238Yuan L, Yang L, Zhang S, Xu Z, Qin J, Shi Y, Yu P, Wang Y, Bao Z, Xia Y, Sun J, He W, Chen T, Chen X, Hu C, Zhang Y, Dong C, Zhao P, Wang Y, Jiang N, Lv B, Xue Y, Jiao B, Gao H, Chai K, Li J, Wang H, Wang X, Guan X, Liu X, Zhao G, Zheng Z, Yan J, Yu H, Chen L, Ye Z, You H, Bao Y, Cheng X, Zhao P, Wang L, Zeng W, Tian Y, Chen M, You Y, Yuan G, Ruan H, Gao X, Xu J, Xu H, Du L, Zhang S, Fu H, Cheng X. Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study. EClinicalMedicine. 2023 Feb 6;57:101834. doi: 10.1016/j.eclinm.2023.101834. eCollection 2023 Mar.
- Background: PubMed 38503097Tan Y, Zhang Z, Li M, Pan F, Duan H, Huang Z, Deng H, Yu Z, Yang C, Shen G, Qi P, Yue C, Liu Y, Hong L, Yu H, Fan G, Tang Y. MedChatZH: A tuning LLM for traditional Chinese medicine consultations. Comput Biol Med. 2024 Apr;172:108290. doi: 10.1016/j.compbiomed.2024.108290. Epub 2024 Mar 13.
- Individual participant dataSharing planned: Undecided
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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.
Qi Zeng, Doctor principal investigator
Fifth Affiliated Hospital, Sun Yat-Sen University
Questions about this trial
What is this clinical trial studying?
An observational study of Medicine, Chinese Traditional and Artificial Intelligence, run by Fifth Affiliated Hospital, Sun Yat-Sen University.
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 2 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 29 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
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 Yulong Zhang, Doctor (18810550602, zhongxiyi1101@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of a tongue image-based machine learning tool (time frame: 20 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06525025, which also lists 3 linked publications.
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 29 Jul 2024.