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Showing 1-4 of 4 trials
NCT07296952
This is a a type 3 hybrid effectiveness-implementation pilot study to evaluate the PRONTO-EYE intervention, a rideshare transportation program, in patients with diabetic retinopathy with Medicaid insurance on adherence to ophthalmology visits.
NCT07404657
This study is being done to evaluate the performance of a software that uses artificial intelligence to analyze photographs of the retina to help detect diabetic retinopathy. The study will also assess the safety of the software in combination with a fundus camera already available on the market. This software analyzes retinal photographs to detect more than mild diabetic retinopathy in adults with diabetes. The results will be compared to expert human evaluations.
NCT07351786
This study aims to test the impact of new-generation anti-diabetic drugs, such as SGLT2 inhibitors and DPP-4 inhibitors, on the development of diabetic retinopathy (DR). The study hypothesizes that these drugs have protective effects in diabetic retinopathy by delaying its incidence compared to older agents (including metformin) only. Early intervention is critical, as treatment options for advanced stages of DR are limited in terms of their ability to restore impaired vision and their high associated costs. By focusing on delaying the occurrence of diabetic retinopathy, the investigators aim to reduce the burden of DR and improve the quality of life for diabetic patients.
NCT07249307
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.