Clinical Research Directory

Improving Patient-centered Care for Diabetes in Bangladesh Through "Dynamic Integration" of Vision Care on the Supply Side

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.

Preliminary Assessment of an Automated Tool for Diabetic Retinopathy Screening

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.

Improving Patient-centered Care for Diabetes in Bangladesh Through "Dynamic Integration" of Vision Care on the Demand Side

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?

Pivotal Trial for an Automated AI-based System for Detection of Diabetic Retinopathy

Assessment of an autonomous AI system for diabetic retinopathy detection in adults with diabetes using retinal imaging.

A Multicenter Randomized Double-Blind Trial of Chrocare CDSS for Integrated Management of Type 2 Diabetes

This is a multicenter, randomized, double-blind, parallel-group clinical study for adults with type 2 diabetes and early diabetic kidney or retinal complications. A total of 1,200 eligible participants will be randomly divided into two groups at a 1:1 ratio. Participants in the intervention group will receive clinical management suggestions generated by the Chrocare large language model clinical decision support system, while those in the control group will receive standard diabetes care formulated by experienced endocrinologists. All participants will complete 3 study visits over 6 months to test blood glucose, glycated hemoglobin, blood lipids, kidney and eye indicators, and record hypoglycemia or other adverse events. The main goal of this research is to compare whether the AI-assisted management can help more patients reach target blood glucose levels, as well as evaluate the safety of this AI system for long-term diabetes integrated treatment.

Glycemic Velocity and Early Retinal Microvascular Change With GLP-1RA Versus SGLT2i Initiation in Type 2 Diabetes (GLIDE)

The GLIDE study looks at how the small blood vessels of the eye respond during the first months of a new diabetes medicine. Two widely used classes of glucose-lowering drugs, GLP-1 receptor agonists and SGLT2 inhibitors, are compared in adults with type 2 diabetes who have no diabetic retinopathy or only early (mild) diabetic retinopathy. When blood sugar (HbA1c) falls quickly after starting treatment, the retina can undergo a brief, temporary worsening before it stabilizes and benefits over the long term. This study asks whether it is the speed of that blood-sugar reduction, which we call "glycemic velocity," rather than the specific drug, that drives early changes in retinal and choroidal blood flow. Participants are patients whose own physician has decided, independently of the study, to start one of these two drugs for the first time. The study does not choose, provide, or change any medicine; it adds only eye imaging, blood tests, and observation. Each participant is followed with specialized, non-invasive eye scans, optical coherence tomography angiography (OCT-A) and structural/choroidal OCT, together with HbA1c and other measurements, at the start of treatment and again over the following months. The main measurement is the change, from the start of treatment to month 3, in the density of the tiny deep-layer capillaries at the center of the retina, measured on OCT-A. The study will test whether faster HbA1c reduction is linked to greater early change in these vessels and, using statistical mediation analysis, will estimate how much of any difference between the two drug groups is explained by glycemic velocity versus a direct drug effect. If glycemic velocity, a factor physicians can influence by adjusting how quickly treatment is intensified, turns out to drive early retinal change, the findings could guide safer treatment strategies and help identify patients who need closer eye monitoring when starting these medicines.

RGX-314 Gene Therapy Administered in the Suprachoroidal Space for Participants With Diabetic Retinopathy (DR) With and Without Center Involved-Diabetic Macular Edema (CI-DME)

ABBV-RGX-314 is being developed as a novel, potential one-time gene therapy treatment for the treatment of Diabetic Retinopathy (DR) with and without Center-Involved Diabetic Macular Edema (CI-DME). DR is a chronic and progressive complication of diabetes mellitus. It is a sight-threatening disease characterized in the early stages by neuronal and vascular dysfunction in the retina, and later by neovascularization that leads to further deterioration of functional vision. Despite the availability of current treatments, diabetic retinopathy remains the leading cause of vision loss in working-age adults, those between the ages of 20 and 74. Existing treatment with anti-VEGF agents, although shown to be effective, are limited by short therapeutic half-lives, which then require frequent intravitreal injections over the patient's lifetime, resulting in increased risk of associated adverse events and significant treatment burden. Due to the burden of treatment, patients often do not closely adhere to treatment regimens and experience sub-optimal outcomes and a decline in vision.

Relation Between Glycemic Control and Retinal Microvascular Changes in Diabetic Patients Using OCTA

An observational cross-sectional comparative study which will be conducted in the Department of Ophthalmology, Sohag University Hospital, Egypt to detect the relation between glycemic control and retinal microvascular changes in diabetic patients using OCTA

Effectiveness and Cost-Effectiveness Evaluations of AI-Assisted Diagnostic Software (VeriSee) for Ophthalmic Disease Screening

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.

Video Intervention to Improve Understanding of Diabetic Retinopathy at Zuckerberg San Francisco General Hospital and Trauma Center

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

Artificial Intelligence-Aided Screening for Patients With Diabetic Retinopathy and Age-related Macular Degeneration in Family Medicine and Geriatric Medicine Outpatient Clinics

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.

To Evaluate the Efficacy and Safety of N-acetyl Cysteine Administration in Patients With Diabetic Retinopathy

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.

Proteomic Biomarker Identification in Age-Related Macular Degeneration, Diabetic Retinopathy and Retinal Detachment

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.

Retinal Clinical Assessment With AI-derived Quantitative Information

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.

Clinical Efficacy of Implementing an AI-SaMD for Funduscopy Analysis in Patients With Diabetes Mellitus

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.

Biomarkers in Diabetic Retinopathy Treated With Faricimab vs Biosimilar Ranibizumab

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

Study Evaluating the Efficacy and Safety of Chloroprocaine HCl Ophthalmic Gel 3% vs Proparacaine Ophthalmic Solution 0.5% Plus Subconjunctival Lidocaine in Patients Undergoing Intravitreal Injections

This Phase 4, multicenter, randomized, double-masked clinical study evaluates the efficacy and safety of chloroprocaine hydrochloride ophthalmic gel 3% (IHEEZO) compared with routine anesthesia (topical proparacaine 0.5% combined with subconjunctival lidocaine 2%) for ocular surface anesthesia during intravitreal injection procedures. Adult participants scheduled to undergo unilateral intravitreal injection of an FDA-approved anti-vascular endothelial growth factor (anti-VEGF) agent for retinal conditions will be randomized in a 1:1 ratio to receive either IHEEZO with a sham subconjunctival procedure or routine anesthesia. The primary objective is to determine whether IHEEZO is non-inferior to routine anesthesia in achieving successful ocular surface anesthesia, defined as a participant-reported pain score of 0 or 1 (on a 0-5 ordinal pain scale) immediately before and immediately after intravitreal injection. Secondary outcomes include individual and cumulative pain scores, change from baseline in dry eye symptoms measured by the Standard Patient Evaluation of Eye Dryness (SPEED) questionnaire, and ocular safety assessments through Day 7 follow-up.

Artificial Intelligence-assisted Diagnosis in Ophthalmology

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.

Diabetic Retinopathy Prevalence And Risk Factors

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?

Using Implementation Science to Adapt a Targeted Transportation Intervention for Patients With Diabetic Retinopathy (PRONTO-EYE)

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.

Impact of the New Generation Anti-diabetic Drugs on Diabetic Retinopathy

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.

Comparing Single Versus Multiple Anti-VEGF Injections in Diabetic Patients Undergoing Cataract Surgery

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.

Assessment of AI Program 'DRISTi' as a Screening Tool

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.

High-throughput Large-model-based AI-assisted Diagnosis Using OCT

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.