Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database

NCT06525025 | Not Yet Recruiting

• 1 other identifier: ZDWY.ZYZLK.009
• Study Type: observational
• Target: 80,000
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Medicine, Chinese Traditional; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Development of a tongue image-based machine learning tool

  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.

  20 months

Secondary Outcomes (1)

• TCM Constitution Multimodal Model

  20 months

Other Outcomes (1)

• Traditional Chinese Medicine (TCM) Doctor-Patient Dialogue Database

  20 months

Study Arms (3)

Traditional Chinese Medicine Tongue Image Group

Internally, using random allocation, divided into training group and validation group

Other: Observational study, non intervention

Traditional Chinese Medicine Constitution Data Group

Internally, using random allocation, divided into training group and validation group

Other: Observational study, non intervention

Traditional Chinese Medicine Doctor Patient Dialogue Data Group

Data used for fine-tuning traditional Chinese medicine models

Other: Observational study, non intervention

Interventions

Observational study, non intervention OTHER

Observational study, non intervention

Traditional Chinese Medicine Constitution Data Group; Traditional Chinese Medicine Doctor Patient Dialogue Data Group; Traditional Chinese Medicine Tongue Image Group

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

There are no specific restrictions on the disease, gender, or health status of the enrolled population.

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Fifth Affiliated Hospital, Sun Yat-Sen University: lead

Related Publications (3)

• 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.

  PMID: 38627559 BACKGROUND

• Yuan 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.

  PMID: 36825238 BACKGROUND

• Tan 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.

  PMID: 38503097 BACKGROUND

MeSH Terms

Interventions

Observation

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Study Officials

• Qi Zeng, Doctor

  Fifth Affiliated Hospital, Sun Yat-Sen University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Yulong Zhang, Doctor

CONTACT

18810550602; zhongxiyi1101@163.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 18, 2024
• First Posted: July 29, 2024
• Study Start: August 1, 2024
• Primary Completion: August 15, 2024
• Study Completion (Estimated): August 15, 2026
• Last Updated: July 29, 2024

Record last verified: 2024-07