Trials / Not Yet Recruiting

Not Yet RecruitingNCT07249307

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

Study on Key Technologies for High-throughput Large-model-based AI-assisted Diagnosis Using OCT

Status: Not Yet Recruiting
Phase: —
Study type: Observational
Enrollment: 2,000 (estimated)

Sponsor: Peking Union Medical College Hospital · Academic / Other
Sex: All
Age: —
Healthy volunteers: Not accepted

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.

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

Interventions

Type	Name	Description
OTHER	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.

Timeline

Start date: 2025-11-30
Primary completion: 2028-06-15
Completion: 2028-12-31
First posted: 2025-11-25
Last updated: 2025-11-25

Source: ClinicalTrials.gov record NCT07249307. Inclusion in this directory is not an endorsement.