High-throughput Large-model-based AI-assisted Diagnosis Using OCT

Sponsor
Peking Union Medical College Hospital
Study ID
NCT07249307
Status
Not Yet Recruiting

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Conditions

  • Age-Related Macular Degeneration (AMD)
  • Diabetic Retinopathy (DR)
  • Glaucoma
  • Pathologic Myopia
  • Retinal Vein Occlusion (RVO)

Eligibility Criteria

Sex
ALL
Age
N/A - N/A
Healthy Volunteers
Not accepted

Interventions

  • No intervention — OTHER
    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 Details

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.

Key Dates

First listed
Nov 25, 2025
Start date
Nov 30, 2025
Status verified
Nov 2025
Primary completion
Jun 15, 2028
Completion
Dec 31, 2028

Study Design

Enrollment
2,000 participants (estimated)

Arms

  • Arm: Diabetic Retinopathy Cohort
    Patients undergoing routine OCT/OCTA examinations with clinically diagnosed diabetic retinopathy.
  • Arm: Branch Retinal Vein Occlusion Cohort
    Patients with BRVO receiving standard clinical imaging evaluation.
  • Arm: Central Retinal Vein Occlusion Cohort
    Patients with CRVO undergoing OCT/OCTA imaging as part of routine care.
  • Arm: Age-related Macular Degeneration Cohort
    Patients diagnosed with AMD and evaluated using OCT/OCTA.
  • Arm: Pathologic Myopia with Choroidal Neovascularization Cohort
    Patients with pathologic myopia and CNV who undergo OCT/OCTA imaging.
  • Arm: Glaucoma Cohort
    Patients with glaucoma-related optic nerve damage undergoing OCT/OCTA imaging.

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). ]

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