Clinical trial NCT06525025: Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database
Official title: Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database: A Prospective Multicenter Cross-Sectional Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Medicine, Chinese Traditional
Artificial Intelligence
Lead sponsor: Fifth Affiliated Hospital, Sun Yat-Sen University
Sponsor class: OTHER
First posted: 2024-07-29
Enrollment: 80000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: There are no specific restrictions on the disease, gender, or health status of the enrolled population.

Brief 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. 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.

Eligibility criteria: Inclusion Criteria:

* People who come to the hospital for physical examination and medical treatment；
* Participants voluntarily participate in the study.

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Development of a tongue image-based machine learning tool
timeFrame: 20 months
Secondary outcomes:
measure: TCM Constitution Multimodal Model
timeFrame: 20 months
Other outcomes:
measure: Traditional Chinese Medicine (TCM) Doctor-Patient Dialogue Database
timeFrame: 20 months
Locations:
Study officials:
affiliation: Fifth Affiliated Hospital, Sun Yat-Sen University
name: Qi Zeng, Doctor
role: PRINCIPAL_INVESTIGATOR
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