View clinical trials related to Diabetic Retinopathy.
Filter by:A case-control study to assess the association between the risk of diabetic retinopathy in Egyptians and genetic polymorphism of both EPO and SLMAP genes.
With the increase in population and the rising prevalence of various diseases, the workload of disease diagnosis has sharply increased. The accessibility of healthcare services and long waiting times have become common issues in the public health medical system, with many primary patients having to wait for extended periods to receive medical services. There is an urgent need for rapid, accurate, and low-cost diagnostic services.
This is a prospective clinical study. At the Shaoguan Diabetic Eye Screening Programme, patients ages 30-80 will undergo the two diagnostic models: (1) in a remote diagnostic clinic site, patients undergo a self-testing device that provides both color retinal photography (CRP) and optical coherence tomography (OCT) imaging of nondilated pupils, and receive an online consultation provided by a retinal specialist; (2) on a separate day, patients visit the tertiary hospital, undergo traditional imaging of dilated pupils acquired by a non-expert imager using a traditional CRP imaging device at the point of care, and receive a face-to-face consultation provided by a retinal specialist. Within one week of receiving both diagnostic imaging services, patient preferences are assessed and they decide which diagnostic imaging model is preferred.
The purpose of this study is to assess the impact of using autonomous artificial intelligence (AI) system for identification of diabetic retinopathy (DR) and diabetic macular edema on productivity of retina specialists in Bangladesh. Globally, the number of people with diabetes mellitus is increasing. Diabetic retinopathy is a chronic, progressive complication of diabetes mellitus that affects the microvasculature of the retina, which if left untreated can potentially result in vision loss. Early detection and treatment of diabetic retinopathy can prevent potential blindness. Study Aim: To assess the impact of using autonomous artificial intelligence (AI) system for detection of diabetic retinopathy (DR) and diabetic macular edema on physician productivity in Bangladesh. Main study question: Will ophthalmologists with clinic days randomized to use autonomous AI DR detection for all persons with diabetes (diagnosed or un-diagnosed) visiting their clinic system have a greater number of examined patients with diabetes (by either AI or clinical exam), and a greater complexity of examined patients on a recognized grading scale, per physician working hour than those randomized not to have autonomous AI screening for their diabetes population? The investigators anticipate that this study will demonstrate an increase in physician productivity, supporting efficiency for both physicians and patients, while also addressing increased access for DR screening; ultimately, preventing vision loss amongst diabetic patients. The study has the potential to contribute to the evidence base on the benefits of AI for physicians and patients. Additionally, the study has the potential to demonstrate the benefits (and/or challenges) of implementing AI in resource-constrained settings, such as Bangladesh.
With the increasing incidence of proliferative diabetic retinopathy (PDR), subsequent neovascular glaucoma (NVG) has become one of the main causes of blindness in PDR patients, and the intraocular pressure of PDR patients with NVG is often stubborn. For these patients, not only is the effect of drugs in lowering intraocular pressure poor, but the results of surgery are often unsatisfactory. Because of its poor prognosis, clinical research for better strategy is of great significance in the current situation. At present, for such patients, a combination of effective control of intraocular pressure and treatment of the primary disease is often used. The purpose of this study was to investigate the clinical effects of preoperative with/without intraoperative anti-vascular endothelial growth factor (VEGF) drug therapy combined with pars plana vitrectomy (PPV), pan-retinal photocoagulation (PRP), and pressure-reducing valve implantation in patients with NVG secondary to PDR. Furthermore, the changes of neurotrophic factors in the vitreous humor before and after anti-VEGF treatment will be explored.
In this study, the study team utilize virtual reality (VR) to simulate visual impairments of different types and severity in healthy subjects. The platform implements three of the most widespread forms of visual impairment in the United States (US): age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma, each with three levels of severity, (mild, moderate, and severe). At present, glaucoma is further developed toward a multidimensional visual impairment simulation. The platform is utilized: i) to provide a safe, controllable, and repeatable set of environments for development and preliminary testing of electronic travel aids (ETAs) in a variety of conditions (i.e., using the ETA to navigate in the immersed environment); and ii) to equip blind and low vision (BVI) professionals, inclusive of orientation and mobility (O&M) instructors, with a controlled, tunable training platform for skill/capacity building, assessment, and refinement of O&M techniques, as well as visually impaired trainees with a safe and immersive environment to improve their O&M skills and learn novel techniques. Two sets of hypothesis-driven experiments are proposed to assess the feasibility of the platform with respect to these two objectives.
The purpose of this study is to determine if providing in clinic point-of-care autonomous AI diabetic retinopathy exams improves screening rates compared to standard of care referral to an eye care provider, in a randomized control trial in a racially and ethnically diverse cohort of youth.
Diabetic macular edema (DME) is one of the leading causes of visual impairment in patients with diabetes. Fluorescein angiography (FA) plays an important role in diabetic retinopathy (DR) staging and evaluation of retinal vasculature. However, FA is an invasive technique and does not permit the precise visualization of the retinal vasculature. Optical coherence tomography (OCT) is a non-invasive technique that has become popular in diagnosing and monitoring DR and its laser, medical, and surgical treatment. It provides a quantitative assessment of retinal thickness and location of edema in the macula. Automated OCT retinal thickness maps are routinely used in monitoring DME and its response to treatment. However, standard OCT provides only structural information and therefore does not delineate blood flow within the retinal vasculature. By combining the physiological information in FA with the structural information in the OCT, zones of leakage can be correlated to structural changes in the retina for better evaluation and monitoring of the response of DME to different treatment modalities. The occasional unavailability of either imaging modality may impair decision-making during the follow-up of patients with DME. The problem of medical data generation particularly images has been of great interest, and as such, it has been deeply studied in recent years especially with the advent of deep convolutional neural networks(DCNN), which are progressively becoming the standard approach in most machine learning tasks such as pattern recognition and image classification. Generative adversarial networks (GANs) are neural network models in which a generation and a discrimination networks are trained simultaneously. Integrated network performance effectively generates new plausible image samples. The aim of this work is to assess the efficacy of a GAN implementing pix2pix image translation for original FA to synthetic OCT color-coded macular thickness map image translation and the reverse (from original OCT color-coded macular thickness map to synthetic FA image translation).
The purpose of this study is to evaluate the quality of ocular images captured on 3 different cameras of patients with diabetic retinopathy. The study will determine whether diabetic retinopathy assessment is comparable between the cameras. The research is being done to see if a camera takes higher quality pictures over the other cameras. 60 participants will be enrolled into this study. Participants need to have diabetic eye disease. This is a one-time study visit that lasts approximately 1 hour.
This study evaluates the corneal features using anterior segment - optical coherence tomography in patients affected by type 2 diabetes mellitus