Ophthalmic ImagingNCT07127939
Diagnostic Performance of an AI-based Model for TCM Constitution Classification Using Ophthalmic Imaging
An observational study of Ophthalmic Imaging and Deep Learning, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
Trial summary
To evaluate the diagnostic performance of a multimodal deep learning model for identifying biased Traditional Chinese Medicine (TCM) constitutions using ophthalmic imaging Read more about this study
- Who is it for?
- Ages 18 to 45 yearsHealthy volunteers can take part
- Conditions the study is for
- Ophthalmic Imaging, Deep Learning, Artificial Intelligence (AI)
- What is being tested?
- AI-based Ocular Diagnosis Model for TCM Constitutions
- How many people
- 1,024 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2025to 31 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 45 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: Chronic systemic disease patients and healthy subjects from the Second Affiliated Hospital of Nanchang University.
Who may be able to join
- Age: Participants aged between 18 and 60 years;
- 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;
- 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;
- 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;
- Diabetes Mellitus: Participants meeting at least one of the following criteria:
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- 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);
- 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;
- Healthy Subjects: Individuals who have not experienced any systemic diseases within the past year that require continuous medical care, limit daily activities, or both;
- 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;
- 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.
Who cannot join
- Incomplete Clinical Data Supporting Diagnosis: Participants for whom the clinical data necessary to substantiate the diagnosis are incomplete;
- 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;
- 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;
- Pregnancy or Lactation: Individuals who are pregnant or currently breastfeeding.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The Second Affiliated Hospital of Nanchang University
Nanchang, Jiangxi, 330000
Jiang Xiong, +8618170906556, 894040417@qq.com
Fu Gui, +8613879101919, 564436578@qq.com
About this study
Official title: Diagnostic Performance of an Artificial Intelligence Driven Model for Traditional Chinese Medicine Constitution Classification Using Ophthalmic Imaging
To evaluate the diagnostic performance of a multimodal deep learning model for identifying biased Traditional Chinese Medicine (TCM) constitutions using ophthalmic imaging
Read the 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.
- Conditions
- Ophthalmic Imaging, Deep Learning, Artificial Intelligence (AI), Traditional Chinese Medicine Constitution
- Being tested
- AI-based Ocular Diagnosis Model for TCM Constitutions
- Keywords
- Ophthalmic Imaging, Deep Learning, Artificial Intelligence, Traditional Chinese medicine constitution
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-based Ocular Diagnosis Model for TCM ConstitutionsDiagnostic test
AI-based Ocular Diagnosis Model for Identifying Traditional Chinese Medicine (TCM) Constitutions
| Study group | Role | What they receive |
|---|---|---|
| Patients with Chronic Systemic Diseases | This cohort consists of participants with a confirmed diagnosis of one or more chronic systemic diseases. These include cardiovascular diseases, malignant tumors, diabetes mellitus, and chronic respiratory diseases, as defined by the study's inclusion criteria. Receives: Diagnostic Test: AI-based Ocular Diagnosis Model for TCM Constitutions | |
| Healthy Control Group | This cohort consists of volunteers with no history of major chronic systemic diseases. Participants are confirmed to have no conditions requiring continuous medical intervention or causing limitation of daily activities, serving as the control group for this study. Receives: Diagnostic Test: AI-based Ocular Diagnosis Model for TCM Constitutions |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUROC of AI-based model in identifying TCM Constitutions
The area under the receiver operating characteristic of AI-based model in identifying Traditional Chinese Medicine constitutions
Measured: Day 0
Secondary outcomes (1)
Sensitivity and specificity of AI-based model in identifying TCM Constitutions
Sensitivity and specificity of AI-based model in identifying Traditional Chinese Medicine constitutions
Measured: Day 0
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Aug 2025
- Study start1 Jul 2025
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ophthalmic ImagingStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital of Nanchang UniversityThe sponsor's other registered studies.
- 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
- Cross-sectional
- Enrolment
- 1,024 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jian Xiong, Second Affiliated Hospital of Nanchang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07127939
- Sponsor's protocol ID
- [2025] NO.(85)
- Status last verified
- Jul 2025
- First submitted
- 11 Aug 2025
- Last updated
- 17 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Performance of an Artificial Intelligence Driven Model for Traditional Chinese Medicine Constitution Classification Using Ophthalmic Imaging
- ClinicalTrials.gov ID
- NCT07127939
- Acronym / sponsor ID
- [2025] NO.(85)
- Conditions
- Ophthalmic Imaging, Deep Learning, Artificial Intelligence (AI), Traditional Chinese Medicine Constitution
- Keywords
- Ophthalmic Imaging, Deep Learning, Artificial Intelligence, Traditional Chinese medicine constitution
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,024 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital of Nanchang UniversityUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 17 Aug 2025 (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.
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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients with Chronic Systemic Diseases | – | Diagnostic Test: AI-based Ocular Diagnosis Model for TCM ConstitutionsThis cohort consists of participants with a confirmed diagnosis of one or more chronic systemic diseases. These include cardiovascular diseases, malignant tumors, diabetes mellitus, and chronic respiratory diseases, as defined by the study's inclusion criteria. |
| Healthy Control Group | – | Diagnostic Test: AI-based Ocular Diagnosis Model for TCM ConstitutionsThis cohort consists of volunteers with no history of major chronic systemic diseases. Participants are confirmed to have no conditions requiring continuous medical intervention or causing limitation of daily activities, serving as the control group for this study. |
Who may qualify
- Ages
- 18 to 45 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Chronic systemic disease patients and healthy subjects from the Second Affiliated Hospital of Nanchang University.
Key inclusion criteria (11 listed)
- Age: Participants aged between 18 and 60 years;
- 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;
- 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;
- 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;
Key exclusion criteria (4 listed)
- Incomplete Clinical Data Supporting Diagnosis: Participants for whom the clinical data necessary to substantiate the diagnosis are incomplete;
- 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;
- 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;
- Pregnancy or Lactation: Individuals who are pregnant or currently breastfeeding.
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 |
|---|---|---|
| AI-based Ocular Diagnosis Model for TCM ConstitutionsAI-based Ocular Diagnosis Model for Identifying Traditional Chinese Medicine (TCM) Constitutions | Diagnostic test | Healthy Control Group, Patients with Chronic Systemic Diseases |
Primary outcome
AUROC of AI-based model in identifying TCM Constitutions
Time frame: Day 0
The area under the receiver operating characteristic of AI-based model in identifying Traditional Chinese Medicine constitutions
1 secondary outcome
Sensitivity and specificity of AI-based model in identifying TCM Constitutions
Time frame: Day 0
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Aug 2025.
- Individual participant dataSharing planned: No
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ophthalmic Imaging and Deep Learning, run by Second Affiliated Hospital of Nanchang University at 1 site in China.
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 11 inclusion and 4 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: looking for participants now, as of the sponsor's last update on 17 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jiang Xiong (+8618170906556, 894040417@qq.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUROC of AI-based model in identifying TCM Constitutions (time frame: Day 0). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 17 Aug 2025.