Diabetic Retinopathy (DR)NCT07249307
High-throughput Large-model-based AI-assisted Diagnosis Using OCT
An observational study of Diabetic Retinopathy (DR) and Retinal Vein Occlusion (RVO), run by Peking Union Medical College Hospital.
Trial summary
This observational study aims to establish key technologies for high-throughput, large-model-based AI-assisted diagnosis using optical coherence tomography (OCT) and OCT angiography (OCTA). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Diabetic Retinopathy (DR), Retinal Vein Occlusion (RVO), Age-Related Macular Degeneration (AMD)
- How many people
- 2,000 participants
- Where
- No sites listed yet
- Study dates
- 30 Nov 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: This study population consists of patients who undergo OCT and/or OCT angiography (OCTA) examinations as part of routine clinical care at Peking Union Medical College Hospital. Eligible participants are clinically diagnosed with one or more of the following conditions: diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopia with choroidal neovascularization, or glaucoma with optic nerve damage. Both retrospectively collected and prospectively enrolled patients are included. No healthy volunteers or experimental interventions are involved.
Who may be able to join
- 1. Patients of any age or sex who undergo OCT and/or OCT angiography (OCTA) examinations as part of routine clinical care at Peking Union Medical College Hospital.
- 2. Clinical diagnosis of at least one of the following conditions: Diabetic retinopathy, Branch retinal vein occlusion, Central retinal vein occlusion, Age-related macular degeneration, Pathologic myopia with choroidal neovascularization and Glaucoma with optic nerve damage.
- 3. Imaging quality sufficient for analysis based on predefined OCT/OCTA quality control criteria.
- 4. Ability to provide informed consent (for prospective participants), or availability of medical records that meet institutional ethical requirements (for retrospective data).
Who cannot join
- 1. Poor-quality OCT/OCTA images that do not meet analysis standards (e.g., severe motion artifacts, media opacity, incomplete scans).
- 2. Patients unable to cooperate with standard ophthalmic imaging procedures. 3. Any condition judged by investigators to preclude accurate imaging evaluation or reliable diagnostic interpretation.
About this study
Official title: Study on Key Technologies for High-throughput Large-model-based AI-assisted Diagnosis Using OCT
This observational study aims to establish key technologies for high-throughput, large-model-based AI-assisted diagnosis using optical coherence tomography (OCT) and OCT angiography (OCTA). The study will collect real-world OCT/OCTA images and corresponding clinical information from patients with common blinding retinal and optic nerve diseases at Peking Union Medical College Hospital.
A high-throughput diagnostic framework based on large-scale artificial intelligence models will be developed and evaluated. The primary objective is to determine the diagnostic performance of the AI system, including its ability to identify diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopic choroidal neovascularization, and glaucoma-related optic nerve damage.
The results of this study are expected to support the development of standardized, efficient, and scalable AI-assisted diagnostic pathways for OCT imaging in clinical practice.
Read the detailed description
This study investigates key technologies for high-throughput, large-model-based AI-assisted diagnosis using optical coherence tomography (OCT) and OCT angiography (OCTA). OCT/OCTA imaging has become an essential non-invasive tool for detecting and monitoring retinal and optic nerve diseases, yet manual interpretation remains time-consuming, experience-dependent, and limited by inter-observer variability. Recent advances in large artificial intelligence models provide an opportunity to develop scalable, generalizable diagnostic tools that can process large multimodal datasets and support clinical decision-making.
This observational study will enroll patients who undergo routine OCT and/or OCTA examinations at Peking Union Medical College Hospital and who are diagnosed with one or more of the following conditions: diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopic choroidal neovascularization, or glaucoma with optic nerve damage. The study will include both retrospectively collected and prospectively acquired imaging and clinical data, following standardized quality control and data-management procedures.
The high-throughput diagnostic framework will be trained and validated using large-scale image and clinical datasets. Primary outcomes include diagnostic performance metrics such as the area under the receiver operating characteristic curve (AUC). Secondary outcomes include sensitivity, specificity, and lesion-level or structural feature assessment when applicable. No experimental intervention will be introduced, and all imaging and clinical evaluations will follow standard clinical care.
The study aims to produce a robust, clinically relevant benchmark for large-model-based AI systems in OCT/OCTA interpretation and provide technical support for future integration of AI-assisted diagnostic tools into routine ophthalmic practice.
- Conditions
- Diabetic Retinopathy (DR), Retinal Vein Occlusion (RVO), Age-Related Macular Degeneration (AMD), Pathologic Myopia, Glaucoma
- Being tested
- No intervention
- Medical subject headings
- Diabetic Retinopathy, Retinal Vein Occlusion, Macular Degeneration, Glaucoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
This observational study involves no experimental intervention. All OCT and OCTA examinations are performed as part of routine clinical care, and the study only analyzes retrospectively and prospectively collected imaging and clinical data to evaluate a large-model-based AI diagnostic system.
| Study group | Role | What they receive |
|---|---|---|
| Diabetic Retinopathy Cohort | Patients undergoing routine OCT/OCTA examinations with clinically diagnosed diabetic retinopathy. Receives: Other: No intervention | |
| Branch Retinal Vein Occlusion Cohort | Patients with BRVO receiving standard clinical imaging evaluation. Receives: Other: No intervention | |
| Central Retinal Vein Occlusion Cohort | Patients with CRVO undergoing OCT/OCTA imaging as part of routine care. Receives: Other: No intervention | |
| Age-related Macular Degeneration Cohort | Patients diagnosed with AMD and evaluated using OCT/OCTA. Receives: Other: No intervention | |
| Pathologic Myopia with Choroidal Neovascularization Cohort | Patients with pathologic myopia and CNV who undergo OCT/OCTA imaging. Receives: Other: No intervention | |
| Glaucoma Cohort | Patients with glaucoma-related optic nerve damage undergoing OCT/OCTA imaging. Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of the AI-assisted OCT/OCTA model (AUC for multi-disease classification)
Area under the receiver operating characteristic curve (AUC) of the high-throughput large-model-based OCT/OCTA diagnostic system for identifying major retinal and optic nerve diseases, including diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopic choroidal neovascularization, and glaucoma.
Measured: Baseline imaging visit (time of image acquisition and model inference).
Secondary outcomes (2)
Sensitivity and specificity of the AI-assisted OCT/OCTA model
Sensitivity and specificity of the AI system for detecting each target disease (diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopic choroidal neovascularization, and glaucoma) compared with masked clinician consensus.
Measured: At the time of image acquisition and model inference (baseline imaging visit).
Agreement between AI-assisted diagnosis and clinician diagnosis
Level of agreement between the AI system's classification and the final clinical diagnosis by retina and glaucoma specialists, quantified using Cohen's kappa statistics or similar agreement measures.
Measured: At the time of image acquisition and model inference (baseline imaging visit).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Nov 2025
- Study start30 Nov 2025 (estimated)
- Primary completion15 Jun 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 Diabetic Retinopathy (DR)Studies of the same condition, open and under way.
- More trials run by Peking Union Medical College HospitalThe 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
- Cohort
- Time perspective
- Other
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07249307
- Sponsor's protocol ID
- K9164
- Status last verified
- Nov 2025
- First submitted
- 18 Nov 2025
- Last updated
- 25 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Individual participant data (IPD) are not planned for public sharing at this time due to institutional policies and ethical restrictions on releasing identifiable clinical imaging data. De-identified aggregated results may be shared upon reasonable request and in compliance with applicable regulations.
Study at a glance
From the registry record, last updated by the sponsor on 25 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study on Key Technologies for High-throughput Large-model-based AI-assisted Diagnosis Using OCT
- ClinicalTrials.gov ID
- NCT07249307
- Acronym / sponsor ID
- K9164
- Conditions
- Diabetic Retinopathy (DR), Retinal Vein Occlusion (RVO), Age-Related Macular Degeneration (AMD), Pathologic Myopia, Glaucoma
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Study start
- 30 Nov 2025 (estimated)
- Primary completion
- 15 Jun 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 25 Nov 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.
This observational study aims to establish key technologies for high-throughput, large-model-based AI-assisted diagnosis using optical coherence tomography (OCT) and OCT angiography (OCTA). The study will collect real-world OCT/OCTA images and corresponding clinical information from patients with common blinding retinal and optic nerve diseases at Peking Union Medical College Hospital.
A high-throughput diagnostic framework based on large-scale artificial intelligence models will be developed and evaluated. The primary objective is to determine the diagnostic performance of the AI system, including its ability to identify diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopic choroidal neovascularization, and glaucoma-related optic nerve damage.
The results of this study are expected to support the development of standardized, efficient, and scalable AI-assisted diagnostic pathways for OCT imaging in clinical practice.
Detailed description
This study investigates key technologies for high-throughput, large-model-based AI-assisted diagnosis using optical coherence tomography (OCT) and OCT angiography (OCTA). OCT/OCTA imaging has become an essential non-invasive tool for detecting and monitoring retinal and optic nerve diseases, yet manual interpretation remains time-consuming, experience-dependent, and limited by inter-observer variability. Recent advances in large artificial intelligence models provide an opportunity to develop scalable, generalizable diagnostic tools that can process large multimodal datasets and support clinical decision-making.
This observational study will enroll patients who undergo routine OCT and/or OCTA examinations at Peking Union Medical College Hospital and who are diagnosed with one or more of the following conditions: diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopic choroidal neovascularization, or glaucoma with optic nerve damage. The study will include both retrospectively collected and prospectively acquired imaging and clinical data, following standardized quality control and data-management procedures.
The high-throughput diagnostic framework will be trained and validated using large-scale image and clinical datasets. Primary outcomes include diagnostic performance metrics such as the area under the receiver operating characteristic curve (AUC). Secondary outcomes include sensitivity, specificity, and lesion-level or structural feature assessment when applicable. No experimental intervention will be introduced, and all imaging and clinical evaluations will follow standard clinical care.
The study aims to produce a robust, clinically relevant benchmark for large-model-based AI systems in OCT/OCTA interpretation and provide technical support for future integration of AI-assisted diagnostic tools into routine ophthalmic practice.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 6
| Arm | Type | Receives |
|---|---|---|
| Diabetic Retinopathy Cohort | – | Other: No interventionPatients undergoing routine OCT/OCTA examinations with clinically diagnosed diabetic retinopathy. |
| Branch Retinal Vein Occlusion Cohort | – | Other: No interventionPatients with BRVO receiving standard clinical imaging evaluation. |
| Central Retinal Vein Occlusion Cohort | – | Other: No interventionPatients with CRVO undergoing OCT/OCTA imaging as part of routine care. |
| Age-related Macular Degeneration Cohort | – | Other: No interventionPatients diagnosed with AMD and evaluated using OCT/OCTA. |
| Pathologic Myopia with Choroidal Neovascularization Cohort | – | Other: No interventionPatients with pathologic myopia and CNV who undergo OCT/OCTA imaging. |
| Glaucoma Cohort | – | Other: No interventionPatients with glaucoma-related optic nerve damage undergoing OCT/OCTA imaging. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- This study population consists of patients who undergo OCT and/or OCT angiography (OCTA) examinations as part of routine clinical care at Peking Union Medical College Hospital. Eligible participants are clinically diagnosed with one or more of the following conditions: diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopia with choroidal neovascularization, or glaucoma with optic nerve damage. Both retrospectively collected and prospectively enrolled patients are included. No healthy volunteers or experimental interventions are involved.
Key inclusion criteria (4 listed)
- 1. Patients of any age or sex who undergo OCT and/or OCT angiography (OCTA) examinations as part of routine clinical care at Peking Union Medical College Hospital.
- 2. Clinical diagnosis of at least one of the following conditions: Diabetic retinopathy, Branch retinal vein occlusion, Central retinal vein occlusion, Age-related macular degeneration, Pathologic myopia with choroidal neovascularization and Glaucoma with optic nerve damage.
- 3. Imaging quality sufficient for analysis based on predefined OCT/OCTA quality control criteria.
- 4. Ability to provide informed consent (for prospective participants), or availability of medical records that meet institutional ethical requirements (for retrospective data).
Key exclusion criteria (2 listed)
- 1. Poor-quality OCT/OCTA images that do not meet analysis standards (e.g., severe motion artifacts, media opacity, incomplete scans).
- 2. Patients unable to cooperate with standard ophthalmic imaging procedures. 3. Any condition judged by investigators to preclude accurate imaging evaluation or reliable diagnostic interpretation.
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 |
|---|---|---|
| No interventionThis observational study involves no experimental intervention. All OCT and OCTA examinations are performed as part of routine clinical care, and the study only analyzes retrospectively and prospectively collected imaging and clinical data to evaluate a large-model-based AI diagnostic system. | Other | Age-related Macular Degeneration Cohort, Branch Retinal Vein Occlusion Cohort, Central Retinal Vein Occlusion Cohort, Diabetic Retinopathy Cohort, Glaucoma Cohort, Pathologic Myopia with Choroidal Neovascularization Cohort |
Primary outcome
Diagnostic performance of the AI-assisted OCT/OCTA model (AUC for multi-disease classification)
Time frame: Baseline imaging visit (time of image acquisition and model inference).
Area under the receiver operating characteristic curve (AUC) of the high-throughput large-model-based OCT/OCTA diagnostic system for identifying major retinal and optic nerve diseases, including diabetic retinopathy, branch retinal vein occlusion, central retinal vein occlusion, age-related macular degeneration, pathologic myopic choroidal neovascularization, and glaucoma.
2 secondary outcomes
Sensitivity and specificity of the AI-assisted OCT/OCTA model
Time frame: At the time of image acquisition and model inference (baseline imaging visit).
Agreement between AI-assisted diagnosis and clinician diagnosis
Time frame: At the time of image acquisition and model inference (baseline imaging visit).
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 25 Nov 2025.
- Individual participant dataSharing planned: Undecided. Individual participant data (IPD) are not planned for public sharing at this time due to institutional policies and ethical restrictions on releasing identifiable clinical imaging data. De-identified aggregated results may be shared upon reasonable request and in compliance with applicable regulations.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetic Retinopathy (DR) and Retinal Vein Occlusion (RVO), run by Peking Union Medical College Hospital.
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 4 inclusion and 2 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: opening soon, as of the sponsor's last update on 25 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance of the AI-assisted OCT/OCTA model (AUC for multi-disease classification) (time frame: Baseline imaging visit (time of image acquisition and model inference).). 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 25 Nov 2025.