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The goal of this observational study is to define the course of the retinal degeneration in a ZSD patient cohort. The objective of this study is to gather information so the investigators can: 1. define the course of the retinal degeneration in a ZSD patient cohort with retinal degeneration 2. define what tests best monitor the progression of the retinal degeneration 3. generate prognostic information about vision loss in ZSD. At each yearly visit, the participants will answer a functional vision questionnaire, have a physical evaluation, blood test, and participate in a variety of vision tests. The investigators will also collect pertinent medical history. Participants will travel to study site. The study will provide financial support for board and travel.
In this pivotal trial, we aim to perform a prospective study to find the efficacy of iPredict-DR, an artificial intelligence (AI) based software tool on early diagnosis of Diabetic Retinopathy (DR) in the primary care and endocrinology clinics. DR is one of the leading causes of blindness in the United States and other developed countries. Every individual with diabetes is at risk of DR. It does not show any symptoms until the disease is progressed to advanced stages. If the disease is caught at an early stage, it can be prevented, managed, or treated effectively. Currently, screening for DR is done by the Ophthalmologists, which is limited to areas with limited availability. This is also time-consuming and expensive. All of these can be complemented by automated screening and set up the screening in the primary care clinics.
The goal of this clinical trial is to learn if a new screening approach including an artificial intelligence algorithm that analyzes fundus photographs, measurement of eye pressure and visual field testing works to screen for glaucoma. Participants will: Have an image of their fundus (back of the eye) taken as part of their diabetic eye screening Have a measurement of their eye pressure If needed, have a test of their side vision using a headset
This research study is being conducted to improve eye care by using artificial intelligence (AI) to make diabetic eye screenings faster and more accessible. AI technology mimics human decision-making, enabling computers and systems to analyze medication information. Specifically for this screening, AI examines digital images of the eye and based on that information, may identify if a participant has diabetic retinopathy. It can assist doctors in making decisions about a participant's diagnosis, treatment or care plans to improve patient care. This is a collaboration between San Ysidro Health (SYHealth), University of California, San Diego (UC San Diego), and Eyenuk. The Kaiser Permanente Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI) awarded SYHealth funds to demonstrate the value of AI technologies in diverse, real-world settings.
The purpose of this study is to understand if combining Low Vision Rehabilitation (LVR) with Emotional Regulation Therapy (ERT) can help people with inherited retinal diseases (IRDs) that experience emotional distress related to participants' vision loss. The study team hypothesize that treatment with LVR will produce measurable functional gains and that these effects will be enhanced by ERT-linked improvement among the subgroup of IRD patients with elevated vision-related anxiety.
The goal of this proposal is to develop novel HH-SECTR technology for visualizing and quantifying diagnostic disease features in prematurely born infant retinopathy of prematurity (ROP) patients that lead to more informed clinical decision making. Providing depth-resolved vascular information has not been adequately investigated for its diagnostic potential. Furthermore, we seek to identify disease features not currently accessible by standard examination methods to better inform clinical decisions.
Phase 2 study to assess the efficacy of topically administered eyedrops of INV-102 during a 12-week dosing period in subjects with non-center involved DME (NCIDME) associated with NPDR \[Part 1\] and during an 8-week dosing period in subjects with center-involved DME (CIDME) associated with NPDR \[Part 2\].
This study is an open-label, single ascending dose clinical trial in participants who have ABCA4-related retinopathies. This is the first-in-human clinical trial in which ACDN-01 will be evaluated for safety, tolerability, and preliminary efficacy following a single subretinal injection of ACDN-01.
The goal of this research study is to compare two ultrawide field cameras to the gold standard imaging system to evaluate the back of the eye. The main question it aims to answer is the same results and information can be acquired from all of the cameras for evaluating and monitoring inherited retinal diseases (IRDs). Participants will: * undergo pupillary dilation * have photographs taken of the inside of the eyes using three different cameras
In the United States, only 62% of the 37 million people with diabetes receive annual screening exams for diabetic retinopathy. One of the goals of the US Department of Health and Human Services Healthy People 2030 campaign is to increase diabetic retinopathy screening rates to 70.3%. Research indicates that low screening rates are associated with a variety of factors, including income levels, race and lack of access to care. Furthermore, because diabetic retinopathy frequently presents asymptomatically, non-adherence to screening results in postponed disease detection and a higher probability of vision loss. Currently, it is estimated that 9 million adults in the US are affected by diabetic retinopathy, and 1.8 million suffer from vision-threatening diabetic retinopathy. Importantly, the rates of vtDR vary greatly by race, with Hispanic individuals at 7.14% and Black individuals at 8.66%, compared to 3.55% in White individuals. Despite these alarming figures, the disease can be managed and vision loss can often be averted with early disease detection, thus highlighting the importance of increasing screening rates. A clear need exists for a diabetic retinopathy screening tool that can be deployed in primary care settings, addressing the shortage of specialist care and making screening more accessible to underserved populations. OPTDR01 will directly address these issues by providing accessible, high quality screening for diabetic retinopathy. OPTDR01 will automatically detect more than mild diabetic retinopathy (mtmDR) and vision-threatening diabetic retinopathy (vtDR) in diabetic adults who have not previously been diagnosed with mtmDR or vtDR.