The Second Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, 330000, China
Location status: Recruiting
Location contact
Fu Gui
CONTACT
Jiang Xiong
CONTACT
NCT Number: NCT07127939
To evaluate the diagnostic performance of a multimodal deep learning model for identifying biased Traditional Chinese Medicine (TCM) constitutions using ophthalmic imaging
Interested in participating?
Request Info18 year–45 year
All sexes
Observational
Nanchang, Jiangxi, 330000, China
Location status: Recruiting
Fu Gui
CONTACT
Jiang Xiong
CONTACT
The identification of Traditional Chinese Medicine (TCM) constitutions is a central principle of its preventive medicine philosophy (Zhi Wei Bing). Yet, current diagnostic methods-relying on subjective questionnaires and expert interpretation-are not scalable, objective, or efficient enough for widespread clinical use. While artificial intelligence has been applied to traditional inputs like tongue and pulse analysis, these approaches have failed to overcome fundamental issues of data standardization and objectivity. Consequently, a critical gap exists: there is no modern, reliable, and scalable tool to assess TCM constitutions, preventing the full integration of this valuable theory into contemporary healthcare.
This study aims to bridge that gap by developing and validating a novel deep learning model for "intelligent ocular diagnosis" of TCM constitutions. By using highly objective and information-rich ophthalmic imaging, our approach circumvents the subjectivity inherent in traditional methods. The primary objective is to create a fully automated, high-throughput system capable of accurately classifying TCM constitutions. The successful completion of this project will not only provide the first objective tool for TCM constitution analysis but will also establish a powerful framework for the early prediction and personalized management of chronic diseases, creating a new paradigm for integrated Chinese and Western medicine.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Heart Failure: Classified as New York Heart Association (NYHA) functional class II-IV with a left ventricular ejection fraction <40%.Stroke: Diagnosed based on a history of ischemic or hemorrhagic stroke or confirmed through imaging evidence such as MRI or CT scans;
Fasting blood glucose ≥7.0 mmol/L (126 mg/dL),2-hour post-oral glucose tolerance test blood glucose ≥11.1 mmol/L (200 mg/dL),Hemoglobin A1c ≥6.5%,Random blood glucose ≥11.1 mmol/L (200 mg/dL) accompanied by typical symptoms (e.g., polydipsia, polyuria, weight loss);
Exclusion criteria
AI-based Ocular Diagnosis Model for Identifying Traditional Chinese Medicine (TCM) Constitutions
Time frame: Day 0
The area under the receiver operating characteristic of AI-based model in identifying Traditional Chinese Medicine constitutions
Time frame: Day 0
Sensitivity and specificity of AI-based model in identifying Traditional Chinese Medicine constitutions
Contact information is provided by the study sponsor or research team.
Second Affiliated Hospital of Nanchang University
Other
Diagnostic Performance of an Artificial Intelligence Driven Model for Traditional Chinese Medicine Constitution Classification Using Ophthalmic Imaging
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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