An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

Associations Between Life's Essential 8 and Major Ocular Diseases in the American Middle-Aged and Elderly Population

Background:Reactive oxygen species and oxidative stress are increasingly recognized as central drivers in the development of major ocular diseases, including cataracts, age-related macular degeneration, glaucoma, and diabetic retinopathy. The eye’s unique environment—continuous light exposure, high oxygen tension, and abundant photosensitizers—renders it particularly vulnerable to ROS-mediated damage. This narrative review aims to synthesize current evidence on the molecular mechanisms of oxidative stress in ocular disease and highlight emerging therapeutic approaches.Methods:Targeted searches of PubMed, Scopus, and Google Scholar for literature published between 2000 and June 2025 were conducted. Keywords included “oxidative stress”, “reactive oxygen species”, “ocular disease”, “cataract”, “age-related macular degeneration”, “glaucoma”, and “diabetic retinopathy”. Only English-language, peer-reviewed articles were considered. Relevant primary studies, clinical trials, reviews, and experimental reports were selectively incorporated, with an emphasis on recent publications and high-impact contributions to the field.Results:Evidence consistently demonstrates that ROS induce lipid peroxidation, protein oxidation, DNA damage, mitochondrial dysfunction, and disruption of redox-sensitive cellular signaling pathways across ocular tissues. In cataracts, oxidation of crystalline proteins and glutathione depletion are primary drivers of lens opacification. In age-related macular degeneration, mitochondrial dysfunction and lipofuscin accumulation promote retinal pigment epithelium degeneration and neovascularization. Glaucoma involves both trabecular meshwork oxidative injury, contributing to elevated intraocular pressure, and mitochondrial-driven retinal ganglion cell apoptosis. In diabetic retinopathy, hyperglycemia-induced ROS overload activates pathogenic pathways, leading to microvascular damage and neuronal dysfunction. Clinical and experimental studies support antioxidant therapies as adjunctive strategies, with the strongest evidence for Age-Related Eye Disease Study-based formulations in age-related macular degeneration and promising results for agents such as Coenzyme Q10 in glaucoma and sulforaphane in diabetic retinopathy. For cataracts, supplementation trials have yielded mixed outcomes and surgery remains the definitive treatment.Conclusions:Oxidative stress represents a unifying mechanism in the pathogenesis of vision-threatening ocular diseases. Antioxidant-based interventions show potential, particularly when integrated with existing treatment regimens, but their translation into routine practice remains limited by heterogeneous trial results and the absence of robust biomarkers for patient selection. Future research should focus on precision antioxidant therapy, leveraging stage-specific interventions, novel delivery systems, and pathway-targeted compounds, to transform ocular care from reactive management toward prevention.

Retinal vessel responses to flickering light are different in various systemic and ocular diseases and can be improved after successful therapy. We investigated retinal vessel response to flickering light in age-related macular degeneration (AMD) patients before and after treatment with a single intravitreal bevacizumab (Avastin(®) ) injection. In 10 patients with exudative AMD [age: median (1.quartile; 3.quartile) 76.0 (73.5; 80.0) years], retinal vessel reactions were examined by Dynamic Vessel Analyser (DVA) before and 3 months after a single intravitreal application of bevacizumab (1.25 mg). A baseline measurement was followed by three consecutive monochromatic flicker stimulations (530-600 nm, 12.5 Hz, 20 seconds). Temporal retinal vessel reaction was analysed and compared with the reaction in healthy controls. Mean arterial dilation at the end of flicker was not different in all groups. For veins this parameter amounted to: pre-treatment, 2.6 (1.7; 3.9)%; post-treatment, 2.9 (2.4; 4.0)%; control, 4.3 (3.2; 5.7)%; significant: pre-treatment - control (Dunnett's procedure, p < 0.05). Maximal dilation occurred in arteries at: pre-treatment, 17.5 (14.8; 32.5) seconds; post-treatment, 18.0 (16.6; 30.6) seconds; control, 14.5 (10.8; 17.3) seconds. Both AMD groups were slower (p < 0.05): in veins at 17.0 (14.5; 20.0) seconds, 12.8 (8.6; 14.8) seconds and 18.5 (17.1; 19.9) seconds, respectively; significant post-treatment - control (p < 0.05). In the post-treatment AMD group arterial constriction after stimulation occurred more slowly compared with the control group (p < 0.05). Dynamic retinal arterial and venous reactions to flickering light are altered in AMD compared with controls. Three months after a single injection of a vascular endothelial growth factor inhibitor, the investigated retinal dynamic vascular parameters were not altered in our study.

Early prediction and timely diagnosis of ocular diseases are of great significance for preventing vision loss and improving patients' quality of life. However, many eye diseases exhibit atypical symptoms in their early stages, leading to delays in clinical diagnosis and consequently postponing treatment opportunities, which may worsen the condition. Artificial intelligence (AI), particularly deep learning, has provided novel solutions for the automated detection and prediction of ophthalmic diseases. Methods such as convolutional neural networks (CNNs), transfer learning, generative adversarial networks (GANs), recurrent neural networks (RNNs), attention mechanisms, and interpretable deep learning have been widely applied in the analysis of fundus and optical coherence tomography (OCT) images. These approaches could extract subtle features that are difficult to capture using traditional techniques, achieving outstanding performance in the diagnosis and grading of diseases such as diabetic retinopathy, glaucoma, and macular degeneration. Therefore, a systematic analysis of the current applications and development trends of AI‐based methods in ocular disease prediction, along with a discussion of their advantages, limitations, and future improvement directions in alignment with clinical needs, will provide valuable insights for advancing intelligent diagnostic research and practice in retinal diseases.

The contributory roles of vitamin D in ocular and visual health have long been discussed, with numerous studies pointing to the adverse effects of vitamin D deficiency. In this paper, we provide a systematic review of recent findings on the association between vitamin D and different ocular diseases, including myopia, age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), dry eye syndrome (DES), thyroid eye disease (TED), uveitis, retinoblastoma (RB), cataract, and others, from epidemiological, clinical and basic studies, and briefly discuss vitamin D metabolism in the eye. We searched two research databases for articles examining the association between vitamin D deficiency and different ocular diseases. One hundred and sixty-two studies were found. There is evidence on the association between vitamin D and myopia, AMD, DR, and DES. Overall, 17 out of 27 studies reported an association between vitamin D and AMD, while 48 out of 54 studies reported that vitamin D was associated with DR, and 25 out of 27 studies reported an association between vitamin D and DES. However, the available evidence for the association with other ocular diseases, such as glaucoma, TED, and RB, remains limited.

We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects—15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing—168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.

RETRACTED: Deep learning network (DL-Net) based classification and segmentation of multi-class retinal diseases using OCT scans

Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.

High-Definition and 3-dimensional Imaging of Macular Pathologies with High-speed Ultrahigh-Resolution Optical Coherence Tomography

Cardiovascular Risk and Eye Health: A Cohort Study of the Pooled Cohort Equations and Ocular Disease Incidence.

Artificial intelligence (AI) has revolutionized optical coherence tomography (OCT) imaging for the better detection of retinal diseases like diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Here, bibliometric analysis of AI in OCT is presented. We conducted a systematic search in the Web of Science Core Collection (WoSCC) database up to January 22, 2025 with the help of AI and OCT-related terms. The journal articles and reviews on the topic were examined based on publication trends, author productivity, institution affiliation, and keyword co-occurrence. Visualization was performed through VOSviewer software (1.6.20). A total of 765 articles were analyzed with an increasing trend in publications, led by the USA (30.7%), China (23.3%), and the UK (11.1%) in research production. The most cited institution was the Medical University of Vienna (9.0%), and the most productive journal was Scientific Reports (7.3%). The most common keywords were "Optical Coherence Tomography", "Deep Learning", and "Diabetic Retinopathy" with frequency of 469, 297, 221, respectively. Our review of AI-enhanced OCT for retinal diseases was both comprehensive and systematic, identifying key trends and current research hotspots. As research productivity in this field continues to grow, the focus is increasingly shifting toward developing more accurate AI-driven imaging techniques to improve the diagnosis of various retinal diseases. Future work should prioritize algorithm validation and clinical implementation to facilitate widespread adoption

Purpose: To review the evolving advancements and clinical applications of artificial intelligence (AI) in ophthalmology, focusing on its impact on diagnostics, treatment, and accessibility to eye care. Methods: A comprehensive literature review was conducted using PubMed and Google Scholar, with search terms including AI, machine learning (ML), deep learning (DL), and ophthalmology-related conditions such as diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), and cataracts. The review covered studies published between 2000 and 2024, including only full-text publications in English. Three reviewers independently selected relevant studies using a hierarchical approach, starting with abstracts and titles, followed by a detailed review of the full texts. Studies focusing on AI’s applications in ophthalmology were analyzed for diagnostic accuracy, efficiency, and implementation challenges. Results: AI technologies, particularly ML and DL, have shown significant promise in ophthalmology. Convolutional neural networks demonstrated expert-level performance in detecting retinal diseases such as DR and AMD. AI-based automated screening systems, predictive analytics, and image quality enhancement tools have improved early detection and management of ocular disorders. Despite these advancements, challenges persist, including ethical concerns, data limitations, algorithm bias, and economic constraints, particularly in low-resource settings. Conclusion: AI is transforming ophthalmology by enhancing diagnostic accuracy and expanding accessibility to eye care. While current applications show immense potential, addressing challenges related to data standardization, algorithm bias, and cost-effectiveness is crucial. Prospects include advancements in personalized medicine, remote monitoring, and collaborative research, offering the potential to revolutionize global eye care delivery.

To study the prevalence and age- and gender-related gradient of ophthalmic diseases (cataract, glaucoma, age-related macular degeneration, diabetic and hypertensive retinopathy) in the Russian population older than 50 years. A random population cohort (9360, aged 45-69 years) was examined in Novosibirsk in 2003/05 and re-examined in 2015/17 (the HAPIEE project). Ophthalmological parameters were studied in a random subsample (324 m/f, aged 55-84, 2015/17). Clinical and instrumental methods were applied to assess eye diseases: cataract, glaucoma, age-related macular degeneration (AMD), diabetic retinopathy (DR), hypertensive retinopathy (HR) and other ophthalmic diseases. In the studied sample of men and women aged 55-84 years, the prevalence of cataract was 72.8%, AMD - 25.7%, glaucoma - 6.2%, DR - 2.1%, HR - 79.6%. The prevalence of other ophthalmic diseases (myopia, hyperopia, astigmatism, pterygium, pinguecula, dystrophy of cornea and retina, tumors and inflammatory diseases) was 13.5%. The frequency of cataract (p<0.001) and AMD (p=0.003) significantly increased with age. The frequency of glaucoma did not change substantially in the studied age range with insignificant increase in those older than 80 years (14.3%). The frequency of DR and HR were not consistently related to age. In the studied population sample of men and women aged 55-84 years (Novosibirsk), the prevalence of cataract and HR is high; the prevalence of AMD is similar to populations with moderate frequency of diseases; the frequency of glaucoma is close to populations with high prevalence of this disease. The frequency of cataract and AMD increased with age. There was no consistent age gradient of the frequency of glaucoma, DR and HR in the age range of 55-84 years.

The endoplasmic reticulum (ER) functions as a quality-control organelle for protein homeostasis, or “proteostasis”. The protein quality control systems involve ER-associated degradation, protein chaperons, and autophagy. ER stress is activated when proteostasis is broken with an accumulation of misfolded and unfolded proteins in the ER. ER stress activates an adaptive unfolded protein response to restore proteostasis by initiating protein kinase R-like ER kinase, activating transcription factor 6, and inositol requiring enzyme 1. ER stress is multifaceted, and acts on aspects at the epigenetic level, including transcription and protein processing. Accumulated data indicates its key role in protein homeostasis and other diverse functions involved in various ocular diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, retinitis pigmentosa, achromatopsia, cataracts, ocular tumors, ocular surface diseases, and myopia. This review summarizes the molecular mechanisms underlying the aforementioned ocular diseases from an ER stress perspective. Drugs (chemicals, neurotrophic factors, and nanoparticles), gene therapy, and stem cell therapy are used to treat ocular diseases by alleviating ER stress. We delineate the advancement of therapy targeting ER stress to provide new treatment strategies for ocular diseases.

Age-related macular degeneration (ARMD) is a degenerative eye disease among people over the age of 50. This disorder affects the retina and macula of the eye. The macula is the central part of the retina. The function of the macular region is to give a sharp and clear vision. ARMD is mainly identified by the presence of yellow deposits called drusen and the structural changes in the retinal pigment epithelium (RPE) layer of the retina. If ARMD is not identified at an early stage, it will lead to permanent blindness. The ARMD is mainly diagnosed using Color fundus and Optical coherence tomography (OCT) images of the retina. OCT is an imaging technique that makes use of low-coherence light to capture two-and three-dimensional images from within optical scattering media. OCT images usually are at a micrometer resolution, affected by a noise called speckle noise. If speckle noise is present in an OCT image, diagnosis of ARMD is very difficult, irrespective of whether it is manually or automatically. So, removal of speckle-noise is significant for the accurate diagnosis and early detection of ARMD. In this paper, we evaluate the performance of eight popular image noise reduction filters for its ability to remove speckle noise in terms of different performance measures on a publicly available OCT dataset. Our evaluation shows that, out of the filters selected for assessment, a fast non-local mean filter outperforms all other filters regarding all the performance measures used.