High-throughput Large-model-based AI-assisted Diagnosis Using OCT
NCT ID: NCT07249307
Last Updated: 2025-11-25
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
2000 participants
OBSERVATIONAL
2025-11-30
2028-12-31
Brief Summary
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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.
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Detailed Description
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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
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Study Design
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COHORT
OTHER
Study Groups
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Diabetic Retinopathy Cohort
Patients undergoing routine OCT/OCTA examinations with clinically diagnosed diabetic retinopathy.
No intervention
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.
Branch Retinal Vein Occlusion Cohort
Patients with BRVO receiving standard clinical imaging evaluation.
No intervention
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.
Central Retinal Vein Occlusion Cohort
Patients with CRVO undergoing OCT/OCTA imaging as part of routine care.
No intervention
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.
Age-related Macular Degeneration Cohort
Patients diagnosed with AMD and evaluated using OCT/OCTA.
No intervention
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.
Pathologic Myopia with Choroidal Neovascularization Cohort
Patients with pathologic myopia and CNV who undergo OCT/OCTA imaging.
No intervention
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.
Glaucoma Cohort
Patients with glaucoma-related optic nerve damage undergoing OCT/OCTA imaging.
No intervention
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.
Interventions
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No intervention
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.
Eligibility Criteria
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Inclusion Criteria
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).
Exclusion Criteria
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.
ALL
No
Sponsors
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Peking Union Medical College Hospital
OTHER
Responsible Party
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Other Identifiers
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K9164
Identifier Type: -
Identifier Source: org_study_id
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