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Active clinical trials for "Retinal Diseases"

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Ultrasound to Detect Evidence for Retinal Detachment in Retinopathy of Prematurity

Retinopathy of Prematurity

Premature infants stand a risk of danger to the layer of the eye that creates sight that, if untreated, can cause severe vision problems, leading to blindness in some cases. This research study will use ultrasound to examine the eye for retinal changes of prematurity.

Completed2 enrollment criteria

Evaluation and Treatment of Patients With Inflammatory Eye Diseases

ChoroiditisIridocyclitis2 more

This study offers evaluation and treatment for patients with inflammatory eye diseases, such as uveitis. The protocol is not designed to test new treatments; rather, patients will receive current standard of care treatments. The purpose of the study is twofold: 1) to allow National Eye Institute physicians to increase their knowledge of inflammatory eye conditions and identify new avenues of possible research in this area; and 2) to establish a pool of patients who may be eligible for new studies as they are developed. (Participants in this protocol will not be required to join a new study; the decision will be voluntary.) Children and adults with uveitis and other inflammatory eye diseases may be eligible for this study. Candidates will be screened with a medical history, brief physical examination, thorough eye examination and blood tests. The eye examination includes measurements of visual acuity (ability to see the vision chart), eye pressure and dilation of the pupils to examine the lens and retina (back part of the eye). Patients may also undergo the following procedures: Fundus photography - Special photographs of the inside of the eye to help evaluate the status of the retina and evaluate changes that may occur in the future. From 2 to 20 pictures may be taken, depending on the eye condition. The camera flashes a bright light into the eye for each picture. Fluorescein angiography - Procedure to evaluate the eye's blood vessels. A yellow dye injected into an arm vein travels to the blood vessels in the eyes. Pictures of the retina are taken using a camera that flashes a blue light into the eye. The pictures show if any dye has leaked from the vessels into the retina, indicating possible blood vessel abnormality. Participants will be followed at least 3 years. Follow-up visits are scheduled according to the standard of care for the individual patient's eye problem. Vision will be checked at each visit, and some of the screening tests described above may be repeated to follow the progress of disease and evaluate the response to treatment.

Completed2 enrollment criteria

Clinical Decision Support Algorithm to Predict Diabetic Retinopathy

Diabetic Retinopathy

Diabetic retinopathy (DR), a complication of diabetes, is a leading cause of blindness among working-aged adults globally. In its early stages, DR is symptomless, and can only be detected by an annual eye exam. Once the disease has progressed to the point where vision loss has occurred, the damage is irreversible. Consequently, early detection is quintessential in treating DR. Two barriers to early detection are poor patient compliance with the annual exam and lack of access to specialists in rural areas. This research is focused on developing and validating new, cost-effective predictive technologies that can improve early screening of DR. Our overall objective is to develop and implement an entire suite of tools to detect diabetes complications in order to augment care for underserved rural populations in the US and internationally.

Unknown status7 enrollment criteria

Gene Expression in Patients With Epiretinal Membranes

Proliferative Diabetic Retinopathy

The purpose of this study was to investigate the expression of selected genes both in epiretinal membranes (ERMs) and peripheral blood mononuclear cells (PBMCs) from patients with primary and secondary epiretinal membranes in proliferative diabetic retinopathy. Possible correlations between messenger ribonucleic acid (mRNA) levels of these genes were also identified.

Unknown status8 enrollment criteria

AI Classifies Multi-Retinal Diseases

Deep LearningRetinal Diseases

The objective of this study is to establish deep learning (DL) algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. The effectiveness and accuracy of the established algorithm will be evaluated in community derived dataset.

Unknown status3 enrollment criteria

Comparison of Aurora Fundus Camera With Traditional Camera in Diabetic Retinopathy With Visual Artificial...

Diabetic Retinopathy

This study aims to compare the effect of Aurora handheld fundus camera with traditional desktop fundus camera in the fundus photography screening of diabetic patients, and to evaluate the effect of artificial intelligence algorithm in the diagnosis of diabetic retinopathy.

Unknown status6 enrollment criteria

Retrospective Review of Proliferative Diabetic Retinopathy Patients

Proliferative Diabetic Retinopathy

The primary objective of the protocol is to determine if intravitreal ranibizumab alone decreases retinal neovascularization from Proliferative Diabetic Retinopathy (PDR) with deferred panretinal photocoagulation (PRP) and/or vitrectomy at one year after treatment with ranibizumab has been initiated.

Unknown status12 enrollment criteria

Side-by-Side Comparison of P200TE and Spectral OCT/SLO on Diseased Eyes

Retinal DiseaseGlaucoma

This study is designed to evaluate and compare in-tissue performance of OCT scans on the new Optos P200TE, versus the predicate Optos Spectral OCT/SLO device.

Unknown status10 enrollment criteria

Analysis of Angiogenic Factor Levels in Eyes With Diabetic Retinopathy

Diabetic Retinopathy

This study was conducted to investigate the levels of angiogenic factors and anti-angiogenic factors in the aqueous humor of patients with diabetic retinopathy.

Unknown status9 enrollment criteria

A Multi-center Study on the Artificial Intelligence Enabled Diabetic Retinopathy Screening Based...

Diabetic Retinopathy

Early detection and intervention of diabetic retinopathy (DR) is critical in preventing DR-related vision loss among type 1 (T1DM) and type 2 diabetic mellitus (T2DM) patients, currently estimated at over 100 million in China alone. Yet the healthcare resources, particularly retinal specialists, are in short supply and unevenly distributed. In order to help address this enormous mismatch and implement population-based screening, an artificial intelligence (AI) enabled, cloud based software is developed by training a custom-built convolutional neural network. This study is designed to evaluate the safety and efficacy of such device in detecting referable diabetic retinopathy (moderate non-proliferative DR or worse).

Unknown status15 enrollment criteria
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