Artificial Intelligence (AI), Deep Learning, Ophthalmic Imaging, Traditional Chinese Medicine Constitution
Conditions
Keywords
Ophthalmic Imaging, Deep Learning, Artificial Intelligence, Traditional Chinese medicine constitution
Brief summary
To evaluate the diagnostic performance of a multimodal deep learning model for identifying biased Traditional Chinese Medicine (TCM) constitutions using ophthalmic imaging
Detailed description
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.
Interventions
DIAGNOSTIC_TESTAI-based Ocular Diagnosis Model for TCM Constitutions
AI-based Ocular Diagnosis Model for Identifying Traditional Chinese Medicine (TCM) Constitutions
Sponsors
Second Affiliated Hospital of Nanchang University
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 45 Years
Healthy volunteers
Yes
Inclusion criteria
1. Age: Participants aged between 18 and 60 years; 2. Chronic Systemic Diseases: Defined according to the standards of the United States National Center for Chronic Disease Prevention and Health Promotion and the Healthy China 2030 planning outline issued by the State Council. These are characterized as conditions lasting one year or longer that require continuous medical care, limit daily activities, or both. The four major chronic diseases in China include:Cardiovascular and cerebrovascular diseases,Malignant neoplasms,Diabetes mellitus and Chronic respiratory diseases; 3. Cardiovascular and Cerebrovascular Diseases: This category includes:Hypertension: Stable hypertension without acute episodes, defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or currently undergoing antihypertensive treatment.Coronary Heart Disease: Diagnosed based on a history of coronary heart disease or confirmed through imaging evidence such as coronary angiography or CT angiography. 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; 4. Malignant Neoplasms: Participants with newly diagnosed, recurrent, metastatic malignant tumors, or those currently receiving specific treatments (e.g., chemotherapy, radiotherapy, immunotherapy), confirmed by histological or cytological examinations; 5. Diabetes Mellitus: Participants meeting at least one of the following criteria: 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); 6. Chronic Respiratory Diseases: This category includes:Chronic Obstructive Pulmonary Disease (COPD): Post-bronchodilator FEV₁/FVC \<0.70 and FEV₁ \<80% of the predicted value, accompanied by at least one clinical symptom such as chronic cough, sputum production, or dyspnea.Asthma: Reversible airflow limitation, defined as a post-bronchodilator FEV₁ increase of ≥12% and ≥200 ml, accompanied by at least one clinical symptom such as recurrent wheezing, dyspnea, chest tightness, or cough; 7. Healthy Subjects: Individuals who have not experienced any systemic diseases within the past year that require continuous medical care, limit daily activities, or both; 8. Clear Diagnosis Based on Traditional Chinese Medicine and Western Medicine: Participants must have a definitive diagnosis established through both Traditional Chinese Medicine and Western Medicine methodologies; 9. Absence of Other Ocular or Systemic Organic Diseases: Participants should have no other ocular diseases and/or systemic organic lesions that significantly affect the acquisition of ocular imaging.
Exclusion criteria
1. Incomplete Clinical Data Supporting Diagnosis: Participants for whom the clinical data necessary to substantiate the diagnosis are incomplete; 2. Significant Opacification of Other Ocular Optical Systems: Individuals presenting with pronounced obscurations in other ocular optical systems (e.g., severe corneal opacification, nuclear cataract grade IV or higher, severe vitreous hemorrhage or opacification) that impede the completion of comprehensive ocular examinations; 3. Acute Systemic Organic Diseases: Patients concurrently suffering from acute systemic organic conditions (such as acute infections or decompensated organ failure) that prevent participation in the constitution identification procedures; 4. Pregnancy or Lactation: Individuals who are pregnant or currently breastfeeding.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUROC of AI-based model in identifying TCM Constitutions | Day 0 | The area under the receiver operating characteristic of AI-based model in identifying Traditional Chinese Medicine constitutions |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity and specificity of AI-based model in identifying TCM Constitutions | Day 0 | Sensitivity and specificity of AI-based model in identifying Traditional Chinese Medicine constitutions |
Countries
China
Contacts
Primary ContactJiang Xiong
Outcome results
None listed