Diabetic Retinopathy (DR)

N-acetylcysteine (NAC) emerges as a crucial factor in mitigating oxidative stress and inhibiting vascular endothelial activation in diabetic patients, through its effect on VEGF expression as VEGF is involved in the development of diabetic microvascular complications through the promotion of retinal angiogenesis and increased vascular permeability. It was reported in the literature that NAC administration was safe in several studies. It was shown that the dose of 1200 mg is safe and effective. To the best of our knowledge, the present study is the first to be designed to evaluate the effect of N-acetyl cysteine on diabetic retinopathy in type 2 diabetic patients.

This prospective interventional translational study aims to identify and validate protein biomarkers associated with major ophthalmological diseases, including age-related macular degeneration, diabetic retinopathy (DR), and retinal detachment (RD). A total of approximately 260 participants (cases and controls) will be enrolled at a single center. Biological samples, including peripheral blood, tears, aqueous humor, vitreous humor, and subretinal fluid, will be collected during routine clinical and surgical procedures. Advanced clinical proteomics approaches will be applied to characterize molecular signatures associated with disease onset, progression, and response to treatment. The study seeks to improve the understanding of disease pathophysiology and support the development of novel diagnostic and prognostic biomarkers in ophthalmology.

This randomized controlled trial evaluates whether providing clinicians with AI-derived quantitative retinal information improves the quality and efficiency of retinal clinical assessment. Participating ophthalmologists and ophthalmology trainees will be randomly assigned to one of two groups. The intervention group will write clinical reports with access to automated quantitative measurements generated from fundus image analysis, including multiple retinal structural and vascular biomarkers. The control group will complete the same reporting tasks using only the original fundus images without AI-generated quantitative information. All reports produced by both groups will be de-identified and independently evaluated by a separate panel of senior ophthalmologists who are blinded to group allocation. The expert evaluators will assess report accuracy, completeness, clarity, and overall clinical quality using predefined scoring criteria. The study aims to determine whether access to quantitative retinal biomarkers enhances clinicians' reporting performance and reduces reporting time during retinal assessment tasks.

The objective of this study is to investigate the efficacy of implementing the AI-SaMD(VUNO Med®-Fundus AI™) alongside routine clinical practice for the detection of diabetic retinopathy.

The study will evaluate whether videos can improve understanding of diabetic eye disease, and follow-up rates in the eye clinic.

The goal of this clinical trial is to see if treatment with faricimab or biosimilar ranibizumab leads to different early imaging changes in adults with diabetic retinopathy requiring anti-VEGF treatment and to identify OCT and OCTA biomarkers predictive of differential early treatment response between the two therapies. The main questions it aims to answer are: Which OCT and OCT angiography biomarkers predict early treatment response? How do imaging biomarkers change after three loading doses of treatment? Are imaging biomarkers associated with systemic laboratory parameters? Researchers will compare faricimab to biosimilar ranibizumab to see if there are differences in imaging biomarkers and early treatment response. Participants will: * be randomized in a 1:1 ratio using a computer-generated randomization sequence * undergo comprehensive ophthalmic examinations, including visual acuity and intraocular pressure measurement * undergo OCT and OCT angiography imaging at each visit * receive three intravitreal injections during the loading phase * attend follow-up visits from baseline to 4-5 weeks after the third injection * provide blood samples for systemic laboratory analysis

This is a retrospective, multicenter, observational study designed to develop and validate an artificial intelligence (AI) system capable of detecting and classifying major ophthalmic diseases (glaucoma, cataract, diabetic retinopathy, and other retinal pathologies) in the Costa Rican population. The study will use approximately 15,000 existing medical images from digital archives of two ophthalmic centers in Costa Rica, without active participant recruitment or capture of new images. The primary motivation is that AI systems developed in other countries (primarily Asian, European, or North American populations) do not necessarily perform with the same accuracy when applied to Latin American populations. This study seeks to establish a precedent for the importance of locally validating any medical AI technology before clinical implementation.

The goal of this retrospective observational study is to evaluate the prevalence and severity of diabetic retinopathy (DR) and to identify associated risk factors among patients with diabetes mellitus at Gia Dinh Family Hospital in Da Nang, Vietnam. The main questions it aims to answer are: 1. What is the prevalence and distribution of different stages of diabetic retinopathy among diabetic patients at this hospital? 2. Which clinical and laboratory factors are associated with the presence of diabetic retinopathy?

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.

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.

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.

Objective of this randomised controlled trial is to compare the efficacy of a single per operative anti VEGF injection with repeated postoperative anti VEGF injections in the prevention of diabetic retinopathy progression after cataract surgery. This Randomised Controlled Trial (RCT) will be conducted at Sahiwal Teaching Hospital and University College of Medicine \& Dentistry Lahore. Duration of study will be from January 2026 to September 2026. This study will be single blind and parallel group research. The study will include diabetic patients of both sexes ≥ 40 years of age presenting with non-proliferative diabetic retinopathy and cataract. Exclusion criteria include proliferative diabetic retinopathy, center-involving diabetic macular oedema (DME), poor glycemic control (HbA1c \>9%), glaucoma, uveitis, prior ocular surgery or laser, and recent systemic thromboembolic events. Subjects will be randomly divided into two groups, each containing 83 subjects. Standard phacoemulsification with intraocular lens implantation will be performed in all participants. A single intra vitreal Aflibercept injection ( 2mg/0.05 ml ), will be given to group 1 participants. While, group 2 participants will receive an intra operative injection plus two additional injections at 1 month and 2 months postoperatively. For all participants, follow up will be performed at 1 week, 1 month, 2 month, 3 month and 6 month. Primary Outcome include progression of diabetic retinopathy (DR) severity (≥1 stage according to the guidelines of the international clinical diabetic retinopathy disease severity scale ICDR) or onset/progression of DME. Secondary Outcomes include changes in best corrected visual acuity, changes in central macular thickness (CMT), and need for rescue treatment. SPSS version 26 will be utilised to analyse the data. P ≤ 0.05 will be taken as statistically significant. Qualitative variable like diabetic retinopathy grading and diabetic macular oedema status will be analysed by utilising Pearson's chi-square test. Quantitative variables like central macular thickness will be analysed by employing t-test.

A study will be conducted to demonstrate that DRISTi will correctly diagnose Diabetic Retinopathy (e.g., mtmDR, PDR, DME) in eyes of patients with diabetes. Participants who have been diagnosed with diabetes mellitus and meet the other inclusion/exclusion criteria will be invited to participate and will consent to have ophthalmic images taken. These images will be analyzed by DRISTi AI software and evaluated by an ophthalmic reading center. The results will be compared, and a statistical analysis will be completed to ensure statistical significance in the outcomes thus proving DRISTi is an effective DR diagnosis tool.

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.

Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are leading causes of vision loss, with rising incidence due to aging populations and increasing diabetes prevalence. However, delayed diagnoses are common due to low disease literacy and lack of dedicated screening tools in internal medicine. This multi-center RCT at National Taiwan University Hospital evaluates the clinical effectiveness and cost-effectiveness of the VeriSee AI-assisted diagnostic software for DR and AMD screening. Participants include adults with diabetes and individuals aged 50 and above meeting AMD screening criteria, randomized to AI-assisted screening with immediate physician explanation or standard physician-only screening. Primary outcomes include detection rates of DR and AMD, ophthalmology referral outcomes, and patient/physician satisfaction. Data collection will occur from April 2025 to December 2027. This study aims to provide evidence on the clinical utility of AI-assisted ophthalmic screening in improving early detection, facilitating timely treatment, and reducing severe visual impairment and healthcare burdens in real-world clinical settings.

The goal of this clinical trial is to determine whether integration of vision care into the DM treatment system, by providing specific recommendations to doctors for management of diabetes alongside feedback on positive eye exams for DR can improve diabetes care outcomes. The main questions it aims to answer are: 1. Does delivery of specific diabetes management recommendations to diabetes doctors for patients of theirs found to have DR lead to significantly improved blood glucose control as indicated by % reaching WHO targets of HbA1c (or targets for blood sugar if HbA1c data are unavailable)? 2. What is the cost-effectiveness of the intervention, measured as total intervention cost per additional participant reaching the WHO target? Researchers will compare participant adherence with international guidelines when DR results are provided by a PIS without specific care recommendations to when the PIS provides DR results with specific care recommendations for DM management.

The goal of this clinical trial is to determine whether there is an increase in participant uptake of diabetes mellitus (DM) and diabetic retinopathy (DR) services and improved quality of patient-centered diabetic care resulting from the use of automated voice message reminders to sensitize people with DM about the potential for sight loss alongside the provision of free reading glasses. The main questions it aims to answer are: 1. Will the proportion of patients completing their scheduled DR and DM appointments within 3 weeks of the scheduled date be significantly increased in the group receiving the automated voice messages containing both appointment reminders and messaging about the importance of visit and medication compliance in reducing the risk of vision loss, compared with participant messages which only remind them of their appointments? 2. What is the cost-effectiveness, measured as total intervention cost per additional patient complying with the suggested exam?

Eye disease affects 2.2 billion people globally, which in turn adversely affects schooling, economic productivity, and participation in social life. The primary conditions contributing to visual impairment and blindness include cataracts, age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), refractive error, and presbyopia. Early detection of eye disease can provide substantial benefits in prompting treatment to reduce progression and mitigate disability. Compared with other regions, South Asia has the most cases of visual impairment due to cataracts and uncorrected refractive error. The combination of poverty, poor living and working environments, and limited health care access have long endangered eye health in Bangladesh. Coastal Bangladesh is particularly impacted by eye disease due to economic deprivation and limited healthcare access. The coastal population mostly works in fishing and agriculture, have prolonged sunlight exposure, and inadequate occupational eye protection. This low-lying region, with 35 million people, is especially vulnerable to climate disasters and global warming. High rates of chronic disease, especially diabetes mellitus Type 2 and hypertension, coupled with limited screening and treatment, shape the area's health profile, with the increasing prevalence of eye diseases such as DR, glaucoma, and visual impairment. To address the issues of poor health, accessibility, and affordability of eye care, Artificial Intelligence (AI) applications, such as Artificial Intelligence (AI)-assisted fundus imaging, can be applied in eye screening. Medical AI applications have the potential to improve the quality and efficiency of healthcare, reduce healthcare costs, optimize treatment plans, and bolster the development of primary healthcare. They can identify presumptive DR, hypertensive retinopathy (HR), AMD, and glaucoma by analyzing the retina and optic disc of fundus images with moderate accuracy and high efficiency, thus helping address the lack of local eye care professionals. Data Yakka developed a human-AI collaboration that delivers affordable and transformative community-based eye screening to underserved communities in the coastal Bangladesh region of Char Fasson. The "Amar Chokh Amar Alo" (My Eyes, My Light) initiative creates and implements comprehensive eye screening that combines AI-assisted eye screening and grassroots partnerships with trusted non-health non-governmental organizations (NGOs). It has three objectives: 1) Enhancing accessibility and affordability of eye screening; 2) Supporting high quality and efficient treatment of those problems detected via screening, 3) Collecting fundus images to refine or train AI algorithms in the future. This project was designed to evaluate the feasibility, performance, equity, and cost of this model of eye screening and its implications for global eye disease. The implementation of participant recruitment, data collection, screening, and follow-up was separated into twelve steps. This standardized framework ensured the integration of screening with data collection and follow-up eye care services. Based on risk stratification by diabetes, hypertension, age 50+ years, and/or optometrist recommendation, fundus imaging was offered selectively to higher-risk patients.

The goal of this observational study is to enroll diabetic patients with diabetic retinopathy and those without diabetic retinopathy, follow up and observe the long-term changes in ocular structure and function of the subjects by comparison, analyze their association with multimodal biomarkers, and explore new methods for the early diagnosis and risk prediction of diabetic retinopathy. Participants will be followed up over a 5-year follow-up period, during which they will undergo ophthalmic examinations, blood tests, and questionnaires.

This study aims to evaluate the effectiveness of an artificial intelligence (AI)-assisted screening system in ophthalmic diagnosis. Using AI-based fundus photography, the system will assist physicians in diagnosing three common eye diseases: age-related macular degeneration and diabetic retinopathy (DR). The AI system will analyze fundus images from participants and rapidly generate detection results for ophthalmologists' reference in making final diagnoses and clinical decisions. The study will assess the clinical benefits of the AI-assisted diagnostic system, providing scientific evidence to enhance the efficiency of ophthalmic disease diagnosis and treatment.

The global distribution of primary ophthalmic medical resources is uneven, and the traditional eye disease screening model has problems such as low efficiency, high cost and limited coverage. With the development of artificial intelligence and other technologies, it provides technical support for the construction of intelligent mobile screening model for eye diseases. The investigator's team has developed the 5G intelligent ophthalmic vehicle and served tens of thousands of people in 108 cities nationwide, initially verifying the feasibility of the new intelligent mobile screening model. However, the application effect, acceptance and influencing factors of this model in different regions are not clear, and there is a lack of economic benefit analysis based on real-world data. In this study, the investigators will conduct a cross-sectional study of large-scale population screening for blinding eye diseases in grassroots areas through the smart mobile screening model, focusing on the screening effectiveness and cost-effectiveness of the smart mobile screening model, integrating real-world multimodal eye health data, developing multiple smart screening analysis models, and exploring its adaptability and direction of improvement in grassroots areas.

Diabetes mellitus is a disorder of sugar and fat metabolism which results in damage to the small blood vessels in various organs, this includes the retina - the part of the eye that processes light into a nerve impulse. This leads to damage and blindness via various different mechanisms that are not fully understood. In this study the objective is to recruit people with diabetes and various stages of diabetic eye disease and measure a large number of different chemicals within the blood that might be associated with damage and dysfunction within the retina. Additionally, it will examine the different bacteria within the gut that might affect disease in the eye via chemicals circulating in the blood. This will require participants to have blood samples taken and to provide urine samples. The blood will be analysed with specialised instruments to identify specific molecules circulating within the blood. Participants will also need to allow researchers to look at their medical records, previous photographs and specialist scans of the back of the eye to grade their diabetic retinopathy. This will allow us to identify potential ways in which these could be targeted for future benefit for people with diabetes to hopefully prevent deterioration of their vision.

The aim of the research project is to validate the use of a novel functional assessment tool designed to document how participants with a vision impairment complete activities of daily living in a real world environment.