Active clinical trials for "Diabetic Retinopathy"

A Double-Masked, Placebo-Controlled Study to Evaluate the Efficacy of Oral AKST4290 in Participants with Moderately Severe to Severe Diabetic Retinopathy (CAPRI).

This is a prospective, observational study designed to evaluate the long-term safety and tolerability of ADVM-022 in participants with diabetic macular edema (DME). Participants who previously participated in the INFINITY parent study and received a single unilateral intravitreal dose of ADVM-022 are eligible for enrollment upon completion of the end of study visit in the parent study.

The purpose of this study is to determine if the reparative cells of blood vessels called endothelial progenitor cells(EPC) are defective in people with diabetes.

Comparison of high-resolution optical coherence tomography (High-Res-OCT) to conventional imaging modalities for the diagnosis of eye diseases

The purpose of this study is to discover early biomarkers in circulating endothelial cells for diabetes complications, by investigating circulating endothelial cells in blood samples from patients with newly diagnosed proliferative diabetic retinopathy, newly diagnosed maculopathy, patients with diabetes without eye diseases, and individuals without diabetes by single-cell RNA sequencing. The single-cell RNA sequencing analysis will make it possible to fully phenotype diabetes circulating endothelial cells at single-cell level and reveal the first atlas of circulating endothelial cells in humans at both healthy and diabetes conditions.

The investigators are conducting a 5-year prospective, cluster-randomized controlled trial, funded by the Centers for Disease Control (CDC), which provides vision screenings to underserved, vulnerable New York City residents living in affordable housing buildings in Harlem and Washington Heights.

All OCTA data and biochemical indexes of diabetic patients were acquired. A prediction model of diabetic retinopathy was built, and the random forest method was used to identify sensitive indicators.

In this pilot study we want to investigate morphological features acquired by the novel image modality and gain information regarding disease pathomechanism, development and future possible influence on disease management for patients affected by those diseases.

Diabetic retinopathy (DR) is the main cause of blindness among working-age adults in the world. Proliferative diabetic retinopathy (PDR) is the severe stage of DR, which is characterized by neovascularization of the retina. Vitreous hemorrhage and traction retinal detachment caused by PDR often require vitrectomy. The purpose of vitrectomy is to remove vitreous hemorrhage, peel off the preretinal proliferative membrane and help restore the retina. With the deepening of people's understanding of diabetes and the development and application of various hypoglycemic drugs, the life expectancy of patients with diabetes continues to prolong. Therefore, the goal of vitrectomy in patients with DR is not only to prevent blindness, but also to maintain good vision for a long time. The purpose of this study is to analyze the risk factors related to the occurrence of long-term low vision postoperatively and establish a risk prediction model, which can help patients adjust their psychological expectations reasonably and promote communication between doctors and patients.

The increasing prevalence of diabetes mellitus represents a major health problem, especially since around 40% of diabetic patients develop diabetic retinopathy, which severely impairs vision and can lead to blindness. This development could be prevented by annual check-ups and timely referral for treatment. However, there are major differences in the quality of examinations and bottlenecks in examination appointments. A solution to the problem could be the use of artificial intelligence (AI), especially deep learning. Initial studies have shown that deep learning algorithms can be used successfully to detect diabetic retinopathy. However, it remains to be clarified whether the use of AI can achieve a sufficiently high level of accuracy in the detection of retinopathies. Therefore, in the present study, the positive predictive value (PPV), the negative predictive value (NPV), the sensitivity (SEN) and the specificity (SPEZ) of the AI algorithm 'MONA-DR-Model' in the detection of diabetic retinopathy should be measured. In addition, it is to be examined how well the classification into mild and severe retinopathy corresponds and how well this new examination method is accepted by the patients.