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High-throughput Large-model-based AI-assisted Diagnosis Using OCT
Sponsor: Peking Union Medical College Hospital
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). 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.
Official title: Study on Key Technologies for High-throughput Large-model-based AI-assisted Diagnosis Using OCT
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
2000
Start Date
2025-11-30
Completion Date
2028-12-31
Last Updated
2025-11-25
Healthy Volunteers
No
Conditions
Interventions
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.