Clinical Research Directory

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

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.

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.

Proteomic Biomarker Identification in AMD, 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 (AMD), 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.

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.

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.

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

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.

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.

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.

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.

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.

Diabetic Retinopathy Screening Point-of-Care Artificial Intelligence

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.

A Real-World Study of Interventional Clinical Outcomes in Patients With Open Angle Glaucoma and Other Chronic Eye Diseases

This is a real world investigation of patient who have undergone treatments for chronic eye conditions such as glaucoma and AMD.

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.

Microperimetry Changes in Retinal Function in Macular Disorders

The purpose of this study is to measure precisely how sensitive the central part of the retina - the light-sensitive film at the back of the eye, is to light. We will use a special device called the Macular Integrity Assessment (MAIA) microperimetry (MP) system to achieve this, particularly for individuals with specific retinal conditions. The macula (with the fovea at its very centre) is the part of the retina responsible for our fine detailed vision, colour vision, and maintaining steady gaze on objects (called 'fixation'). Diseases that affect the macula lead to difficulties in seeing clearly. Macular sensitivity refers to how responsive the macula is to light, including the ability to read and focus on objects. This also determines how well the eye can maintain a steady gaze on objects of interest ('fixation'). While standard eye tests primarily measure vision in the fovea, measuring sensitivity across the wider macula would provide a more complete picture of visual function. Furthermore, we believe that macular sensitivity changes are often one of the earliest signs of retinal diseases, before a person experiences blurry vision. Currently, MP is not used routinely in UK NHS clinical practice. The commonest retinal diseases are age-related macular degeneration (AMD) and diabetic retinopathy (DR). There are 2 types of AMD: dry and wet. * Dry AMD is a condition that affects the central part of the retina (the macula) and can lead to gradual vision loss as people age. This is due to wearing out and loss of the slight sensitive cells in the macula that can make it harder to see fine details, such as reading or recognizing faces. * Diabetic retinopathy means that diabetes has affected the blood vessels in the retina. In the early stages, the affected blood vessels do not leak. However, progression results in leakage of the blood vessels in the retina, leading to retinal swelling (called diabetic macular oedema \[DMO\]). Eyes with DMO function less well compared to when there is no swelling. It is important to investigate these selected common conditions further, in order to find ways of detecting changes earlier, before the patient notices any abnormalities. Such earlier detection may result in better understanding and treatments in the future. The main goal of this research is to measure macular sensitivity and ability to maintain a steady gaze on specific objects or points in patients with these macular disorders using the MAIA device. These measurements will be compared to changes in the structure of the macula, obtained using advanced imaging techniques like optical coherence tomography (OCT) and OCT angiography (OCTA), which are routinely used in standard clinical practice. This study will form part of a research/educational thesis, and provide additional data to complement previous research on the topic. Participants (after consent) will have MP done. In addition, we will assess information from their eye clinic records, including images and scans of the back of the eyes (OCT and OCTA). No treatment interventions are planned as part of this study. Participants standard of care will not be affected. Participants will have tests done at baseline, and repeated at 6 and 12 months. In addition, we will invite a group of normal controls (i.e. persons who do not have any diseases of the back of their eyes) for comparison. This will ensure validity of our findings. The normal controls will attend only once (at baseline). After consent is obtained, these healthy participants will have MP, and imaging of the macula with OCT.

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?

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.

"My Eyes, My Light": Amar Chokh, Amar Alo

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.

Multimodal Biomarkers in Prediction of Diabetic Retinopathy

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.

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.