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.
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.
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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.
Cohort
Other
No intervention
Other 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.
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Treatment groupsSee each treatment group below.
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.
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.
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.
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