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To extract maximum features ResAttNet (RAN) network structure is chosen as an alternative to the convolutional layer and it enhances image feature extraction. Additionally, a Deformable Convolution (DC) network was included to provide a feature extraction module, improving the model's capacity to simulate vessel deformation. Apart from the two additional networks because of inadequate quality in retinal data, before model building pre-processing is done. The data is processed by CLAHE, normalization, grayscale transformation, and gamma transformation. Second, the fundamental network structure model U-net is constructed, and the ResAttNet (RAN) structure and DC network are combined to form the ARDC-UNet network. Experimental data, both quantitative and qualitative, demonstrate the efficiency and accuracy with which our ARDC-UNet can segment retinal vessels. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR is hemorrhages in the retina. Therefore, we propose a new method for accurate hemorrhage detection from retinal fundus images. Here, the proposed method uses the modified contrast enhancement method to improve the edge details from the input retinal fundus images. In the second stage, a deep convolutional neural network (DCNN) is used for feature extraction, and the classification of the hemorrhages is performed using LSTM with Harris Hawks with Mayfly Optimization (HHMO). Finally, the proposed DCNN with HHO- LSTM is compared with the existing techniques including machine learning and deep learning technique such as Naïve Bayes, SVM, ANN, etc., and traditional DCNN, LSTM, and other techniques, respectively. Therefore, the comparison can prove that the proposed model is more effective to detect and classify Hemorrhages in the retina due to diabetic retinopathy. The performance metrics are considering in this work are accuracy (0.9548), specificity (0.9314), sensitivity (0.9230), f1-score (0.731), precision (0.823), etc. [ABSTRACT FROM AUTHOR]

This study aimed to characterize the most updated utilization of eye care services and obtain a holistic understanding of barriers among patients with diabetes in China. This was a convergent mixed methods study. A convergent triangulation mixed methods approach was used, with a quantitative cross-sectional survey of patients with diabetes and semistructured interviews involving patients and health workers. Following the conceptual framework of the World Health Organization Determinants of Health Behaviours, multivariate logistic regression for quantitative analysis and thematic analysis for qualitative data were used to examine barriers to seeking eye care among patients with diabetes. Triangulation was used to integrate quantitative and qualitative results. Among 1167 surveyed patients who participated in the quantitative component, 29.1% had undergone eye examinations within the last 12 months, and 9.3% had received eye surgery. Awareness that diabetes causes eye diseases (P < 0.001) and knowing laser treatment can treat diabetic retinopathy (DR; P < 0.001) were associated with higher examination rates. In the qualitative component, involving 20 patients and 11 health workers, barriers were identified from individual, social, and cultural environmental factors. Integration of data highlighted the complex interplay of these factors in shaping care-seeking behaviors and the importance of non-economic factors, including patients' information about costs of DR services and cultural environmental factors. Diabetic eye care utilization remains suboptimal in China, emphasizing the impact of cultural and contextual factors. Comprehensive education strategies, along with training for primary health workers and task-shifting, are likely to enhance eye care service utilization in underserved settings. [ABSTRACT FROM AUTHOR]

Diabetic eye detection has become a major concern across the globe, which could be effectively addressed by automated detection using a deep convolutional neural network (DCNN). CNN models have better detection and classification accuracy than other state-of-theart models. In this paper, a differential evolution (DE)-optimized CNN has been proposed for the single-step classification of diabetic retinopathy (DR) and glaucoma images. DE has been used to find out the optimized values of four hyper-parameters of CNN, i.e., the number of filters in the first layer, the filter size, the number. of convolution layers, and the number of strides. Simulation has been done using three publicly available datasets, and the accuracy obtained is 87.8%, 92.3%, and 88.7%, respectively, which outperforms other models. No other state-of-the-art model has used DE for hyper-parameter tuning in CNN models. Also, no other additional segmentation approach or handcrafted features have been used. The model has been kept simple to reduce computational costs. [ABSTRACT FROM AUTHOR]

The present-day highly regarded and active ophthalmic research has paved the way for the detection of several retinal problems, aiding the ophthalmologist in planning and carrying out prompt treatment. Regular screening is one of the most challenging responsibilities due to the greater number of patients with retinal defects and the smaller number of medical specialists. Diabetic Retinopathy is a retinal vascular abnormality. Most people with long-term diabetes mellitus will eventually acquire this condition, which may lead to blindness. Here in this research article work retina fundus images are taken from the both Public Messidor, EyePACs dataset and the in-house clinical dataset from Chaithanya Eye Hospital Kerala. The first stage is to remove noise from the input image and enhance the contrast of the images. For noise reduction, a Trilateral Filter is utilized first, followed by enhancement utilizing contrast-limited Adaptive Histogram Equalization with an unsharp technique. Then Thick Blood vessels are segmented from the enhanced image using the Extended Piecewise Fuzzy C-Means Clustering (EPFCMC) Method. From the segmented image, GLCM features are extracted and then features are selected using War strategy optimization. Finally, an ensemble transfer learning using thrice CNN and SVM classifier model is used which classifies the image as Diabetic Retinopathy (DR) or Normal case. Using Thrice CNN and the SVM classifier model, an accuracy of 98.94% is obtained. The results obtained through this framework for diabetic retinopathy classification prove the effectiveness and wide applicability of the proposed approaches. It is also hoped that the developed automatic detection techniques will assist clinicians in diagnosing Diabetic Retinopathy at an early stage. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) stands as one of the most prevalent complications among individuals with diabetes, potentially resulting in irreversible vision loss. Recent efforts within the research community have focused on developing Computer-Aided Diagnosis tools, harnessing color fundus retinal scan images to automate the assessment of diabetic retinopathy severity grades. Leveraging the latest advancements in Computer Vision and Neural Networks, these solutions have demonstrated impressive accuracy in identifying the early stages of retinopathy. However, their performance diminishes when faced with higher severity grades, likely due to the scarcity of labeled data for such cases. This study aims to address this limitation by delving into deep spatial representations derived from color fundus retinal scan images associated with higher severity grades. This is possibly due to the small number of labelled samples available for higher severity grades. Different from the existing approaches, we exploit deep spatial representations extracted from a diverse set of pre-trained deep convolutional neural networks to craft an Adaptive Ensemble Classifier. This novel methodology excels at accurately classifying the severity grades of diabetic retinopathy. Our experiments, conducted on the Kaggle APTOS-2019 benchmark dataset, illustrate the superiority of multi-modal deep spatial representations when utilized in conjunction with the Adaptive Ensemble Classifier, by achieving 81.86% accuracy and surpassing the performance of hand-crafted and uni-modal representations for retinal scan images. These findings offer a promising stride towards enhancing the accuracy of diabetic retinopathy diagnosis, particularly in the context of more advanced severity grades. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusionsDiabetic retinopathy (DR) is one of the most severe and common complications caused by diabetic mellites. Inhibiting NLRP3 inflammasome activation displays a crucial therapeutic value in DR. Studies have shown that KCNQ1OT1 plays a critical role in regulating NLRP3 inflammasome activation and participates in the pathogenesis of diabetic complications. The present study aims to explore the role, and the potential mechanism of KCNQ1OT1 in regulating the activation of NLRP3 inflammasome in DR.qRT-PCR was used to detect the expression of KCNQ1OT1, miR-17-5p, TXNIP, NLRP3, ASC, caspase-1 and IL-1β. Western blot was performed to detect the expression of NLRP3, ASC, caspase-1, IL-1β and TXNIP. Immunohistochemistry and immunostaining were performed to detect the expression of caspase-1. The levels of the inflammatory cytokine IL-1β were determined by ELISA assay. FISH was used to detect the subcellular localisation of KCNQ1OT1. Bioinformatic analysis, luciferase reporter assay and in vitro studies were performed to elucidate the mechanism of KCNQ1OT1-mediated dysfunction.The expression of KCNQ1OT1 and the activation of NLRP3 inflammasome were increased in experimental DR models. KCNQ1OT1 knockdown alleviated NLRP3 inflammasome-associated molecules expression. In addition, KCNQ1OT1 was found to be localized mainly in the cytoplasm of Müller cells and facilitated TXNIP expression by acting as a miR-17-5p sponge. KCNQ1OT1 promoted the activation of NLRP3 inflammasome through miR-17-5p/TXNIP axis.In conclusion, it was found in this study that KCNQ1OT1 promoted the activation of NLRP3 inflammasome both in vitro and in vivo, which was mediated by miR-17-5p/TXNIP axis. KCNQ1OT1 might be an effective interference target for the prevention and treatment of DR. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) and diabetic retinopathy (DR) are common retinal diseases responsible for most blindness in working-age and elderly populations. Oxidative stress and mitochondrial dysfunction play roles in these pathogenesis, and new therapies counteracting these contributors could be of great interest. Some molecules, like coenzyme Q10 (CoQ10), are considered beneficial to maintain mitochondrial homeostasis and contribute to the prevention of cellular apoptosis. We investigated the impact of adding CoQ10 (Q) to a nutritional antioxidant complex (Nutrof Total®; N) on the mitochondrial status and apoptosis in an in vitro hydrogen peroxide (H2O2)-induced oxidative stress model in human retinal pigment epithelium (RPE) cells. H2O2 significantly increased 8-OHdG levels (p < 0.05), caspase-3 (p < 0.0001) and TUNEL intensity (p < 0.01), and RANTES (p < 0.05), caspase-1 (p < 0.05), superoxide (p < 0.05), and DRP-1 (p < 0.05) levels, and also decreased IL1β, SOD2, and CAT gene expression (p < 0.05) vs. control. Remarkably, Q showed a significant recovery in IL1β gene expression, TUNEL, TNFα, caspase-1, and JC-1 (p < 0.05) vs. H2O2, and NQ showed a synergist effect in caspase-3 (p < 0.01), TUNEL (p < 0.0001), mtDNA, and DRP-1 (p < 0.05). Our results showed that CoQ10 supplementation is effective in restoring/preventing apoptosis and mitochondrial stress-related damage, suggesting that it could be a valid strategy in degenerative processes such as AMD or DR. [ABSTRACT FROM AUTHOR]

Vision impairment caused by diabetic retinopathy (DR) is often irreversible, making early‐stage diagnosis imperative. Raman spectroscopy emerges as a powerful tool, capable of providing molecular fingerprints of tissues. This study employs RS to detect ex vivo retinal tissue from diabetic rats at various stages of the disease. Transmission electron microscopy was utilized to reveal the ultrastructural changes in retinal tissue. Following spectral preprocessing of the acquired data, the random forest and orthogonal partial least squares‐discriminant analysis algorithms were employed for spectral data analysis. The entirety of Raman spectra and all annotated bands accurately and distinctly differentiate all animal groups, and can identify significant molecules from the spectral data. Bands at 524, 1335, 543, and 435 cm−1 were found to be associated with the preproliferative phase of DR. Bands at 1045 and 1335 cm−1 were found to be associated with early stages of DR. [ABSTRACT FROM AUTHOR]

Diabetic macular edema (DME), defined as retinal thickening near, or involving the fovea caused by fluid accumulation in the retina, can lead to vision impairment and blindness in patients with diabetes. Current knowledge of retina anatomy and function and DME pathophysiology has taken great advantage of the availability of several techniques for visualizing the retina. Combining these techniques in a multimodal imaging approach to DME is recommended to improve diagnosis and to guide treatment decisions. We review the recent literature about the following retinal imaging technologies: optical coherence tomography (OCT), OCT angiography (OCTA), wide-field and ultrawide-field techniques applied to fundus photography, fluorescein angiography, and OCTA. The emphasis will be on characteristic DME features identified by these imaging technologies and their potential or established role as diagnostic, prognostic, or predictive biomarkers. The role of artificial intelligence in the assessment and interpretation of retina images is also discussed. • Diabetic macular edema (DME) leads diabetic patients to vision loss and blindness. • Multimodal imaging combines various techniques. • Multimodal imaging aids DME diagnosis and guides treatment. • Imaging has potential as diagnostic, prognostic, or predictive biomarkers. • Artificial intelligence can assess and interpret retina images. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes and has become one of the leading causes of blindness and visual impairment in diabetes patients. The pathogenesis of DR is multifaceted, involving inflammation, oxidative stress, neurovascular abnormalities, and other factors that present potential targets for disease management interventions. Currently, anti-vascular endothelial growth factor (VEGF) drugs serve as the primary treatment for advanced stages of DR when irreversible neurovascular damage and visual impairment have occurred. Additionally, some patients show poor or no response to anti-VEGF treatment. There is a lack of early intervention options for the initial phases of the disease. Therefore, there is an urgent need to develop novel local or systemic therapies based on the underlying mechanisms of DR to enable early prevention and treatment with the aim of preserving patients′ vision. Medications targeting various pathways including anti-inflammatory agents (corticosteroids and nonsteroidal anti-inflammatory drugs), neurotrophic and neuroprotective drugs, drugs modulating biochemical pathways, antioxidant phytochemicals, and gene therapy can complement each other in terms of therapeutic effects to benefit a larger number of individuals affected by DR. This article reviews previous research reports on the pathogenesis, drug treatment methods, and potential therapeutic targets associated with DR in order to provide guidance for clinical practice.

Endothelial-to-mesenchymal transition (EndMT) is a pivotal event in diabetic retinopathy (DR). This study explored the role of circRNA zinc finger protein 532 (circZNF532) in regulating EndMT in DR progression. Human retinal microvascular endothelial cells (HRMECs) were exposed to high glucose (HG) to induce the DR cell model. Actinomycin D-treated HRMECs were used to confirm the mRNA stability of phosphoinositide-3 kinase catalytic subunit δ (PIK3CD). The interaction between TATA-box-binding protein-associated factor 15 (TAF15) and circZNF532/PIK3CD was subsequently analyzed using RNA immunoprecipitation (RIP), RNA pull-down. It was found that HG treatment accelerated EndMT process, facilitated cell migration and angiogenesis, and enhanced PIK3CD and p-AKT levels in HRMECs, whereas si-circZNF532 transfection neutralized these effects. Further data showed that circZNF532 recruited TAF15 to stabilize PIK3CD, thus elevating PIK3CD expression. Following rescue experiments suggested that PIK3CD overexpression partially negated the inhibitory effect of circZNF532 silencing on EndMT, migration, and angiogenesis of HG-treated HRMECs. In conclusion, our results suggest that circZNF532 recruits TAF15 to stabilize PIK3CD, thereby facilitating EndMT in DR.

Accurate medical image classification poses a significant challenge in designing expert computer-aided diagnosis systems. While deep learning approaches have shown remarkable advancements over traditional techniques, addressing inter-class similarity and intra-class dissimilarity across medical imaging modalities remains challenging. This work introduces the advanced gating transformer network (MedTransNet), a deep learning model tailored for precise medical image classification. MedTransNet utilizes channel and multi-gate attention mechanisms, coupled with residual interconnections, to learn category-specific attention representations from diverse medical imaging modalities. Additionally, the use of gradient centralization during training helps in preventing overfitting and improving generalization, which is especially important in medical imaging applications where the availability of labeled data is often limited. Evaluation on benchmark datasets, including APTOS-2019, Figshare, and SARS-CoV-2, demonstrates effectiveness of the proposed MedTransNet across tasks such as diabetic retinopathy severity grading, multi-class brain tumor classification, and COVID-19 detection. Experimental results showcase MedTransNet achieving 85.68% accuracy for retinopathy grading, 98.37% ( ± 0.44 ) for tumor classification, and 99.60% for COVID-19 detection, surpassing recent deep learning models. MedTransNet holds promise for significantly improving medical image classification accuracy. [ABSTRACT FROM AUTHOR]

Gene therapy holds promise as a transformative approach in the treatment landscape of age-related macular degeneration (AMD), diabetic retinopathy (DR), and diabetic macular edema (DME), aiming to address the challenges of frequent intravitreal anti-vascular endothelial growth factor (VEGF) injections. This manuscript reviews ongoing gene therapy clinical trials for these disorders, including ABBV-RGX-314, ixoberogene soroparvovec (ixo-vec), and 4D-150. ABBV-RGX-314 utilizes an adeno-associated virus (AAV) vector to deliver a transgene encoding a ranibizumab-like anti-VEGF antibody fragment, demonstrating promising results in Phase 1/2a and ongoing Phase 2b/3 trials. Ixo-vec employs an AAV2.7m8 capsid for intravitreal delivery of a transgene expressing aflibercept, showing encouraging outcomes in Phase 1 and ongoing Phase 2 trials. 4D-150 utilizes an evolved vector to express both aflibercept and a VEGF-C inhibitory RNAi, exhibiting positive interim results in Phase 1/2 studies. Other therapies reviewed include EXG102-031, FT-003, KH631, OLX10212, JNJ-1887, 4D-175, and OCU410. These therapies offer potential advantages of reduced treatment frequency and enhanced safety profiles, representing a paradigm shift in management towards durable and efficacious cellular-based biofactories. These advancements in gene therapy hold promise for improving outcomes in AMD and addressing the complex challenges of DME and DR, providing new avenues for the treatment of diabetic eye diseases. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a major cause of irreversible blindness in the working-age population. Neovascularization is an important hallmark of advanced DR. There is evidence that Yes-associated protein (YAP)/transcriptional co-activator with a PDZ binding domain (TAZ) plays an important role in angiogenesis and that its activity is regulated by vascular endothelial growth factor (VEGF). Therefore, the aim of this study was to investigate the effect of YAP/TAZ-VEGF crosstalk on the angiogenic capacity of human retinal microvascular endothelial cells (hRECs) in a high-glucose environment. The expression of YAP and TAZ of hRECs under normal conditions, hypertonic conditions and high glucose were observed. YAP overexpression (OE-YAP), YAP silencing (sh-YAP), VEGF overexpression (OE-VEGF) and VEGF silencing (sh-VEGF) plasmids were constructed. Cell counting kit-8 assay was performed to detect cells proliferation ability, transwell assay to detect cells migration ability, and tube formation assay to detect tube formation ability. The protein expression of YAP, TAZ, VEGF, matrix metalloproteinase (MMP)-8, MMP-13, vessel endothelium (VE)-cadherin and alpha smooth muscle actin (α-SMA) was measured by western blot. The proliferation of hRECs was significantly higher in the high glucose group compared with the normal group, as well as the protein expression of YAP and TAZ (p < 0.01). YAP and VEGF promoted the proliferation, migration and tube formation of hRECs in the high glucose environment (p < 0.01), and increased the expression of TAZ, VEGF, MMP-8, MMP-13 and α-SMA while reducing the expression of VE-cadherin (p < 0.01). Knockdown of YAP effectively reversed the above promoting effects of OE-VEGF (p < 0.01) and overexpression of YAP significantly reversed the inhibition effects of sh-VEGF on above cell function (p < 0.01). In a high-glucose environment, YAP/TAZ can significantly promote the proliferation, migration and tube formation ability of hRECs, and the mechanism may be related to the regulation of VEGF expression. [ABSTRACT FROM AUTHOR]

This paper provides a systematic survey of artificial intelligence (AI) models that have been proposed over the past decade to screen retinal diseases, which can cause severe visual impairments or even blindness. The paper covers both the clinical and technical perspectives of using AI models in hosipitals to aid ophthalmologists in promptly identifying retinal diseases in their early stages. Moreover, this paper also evaluates various methods for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of the existing literature. This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope. Additionally, this paper serves as a helpful guide for researchers who want to work in the field of retinal image analysis in the future. [ABSTRACT FROM AUTHOR]

Human vision depends heavily on retinal tissue. The loss of eyesight may result from infections of the retinal tissues that are treated slowly or do not work at all. Additionally, the diagnosis is susceptible to inaccuracies when a large dataset is involved. Therefore, a fully automated transfer learning approach for diagnosing diabetic retinopathy (DR) is suggested to minimize human intervention while maintaining high classification accuracy. To address this issue, we proposed a transfer learning-based trilateral attention network (TaNet) for the classification. To boost the visual quality of the DR pictures, a contrast constrained adaptive histogram equalization approach is applied. The pre-processed pictures are then segmented using a bilateral segmentation network (BiSeNet). The BiSeNet segmented the optic disc and blood vessels individually. After the completion of segmentation, the features are extracted. Feature extraction is based on the wavelet scattering transformation approach. The results of many trials were evaluated against the Messidor-2, EYEPACS, and APTOS 2019 datasets. The proposed model was created using a refined pre-trained technique and transfer learning methodology. Finally, the suggested framework was tested using efficiency assessment methods, and the classification rate was recorded as having above 98% sensitivity, specificity, precision, and accuracy. The proposed approach yields greater performance and shows enhancement towards the existing approach.

Diabetic patients are tremendously increasing worldwide and it is a chronic disease that may cause complications in Eye, Heart, and Kidneys. Diabetic retinopathy (DR) plays a vital role that causing vision loss in diabetic patients, if not treated at an earlier stage. Nowadays, a lot of patients undergo eye screening per day, therefore ophthalmologists face a lot of challenges during the screening of Diabetic retinopathy. Also, manual screening leads to errors and is more time-consuming, the patients have to wait for much time in the clinic. Hence an automated system is essential to help the ophthalmologist as a secondary opinion in retinal screening. Normally, clinicians will detect the different signs of DR from the retinal images taken through fundus photography, may leads manual error. Here, this research work proposes a novel automated system through the implementation of deep learning techniques in biomedical analysis. This system includes the following stages pre-processing, segmentation, and classification. For analysis, the proposed research article work on retina fundus images is taken from the both Public dataset and the in-house clinical dataset from Chaithanya Eye Hospital Kerala. The first stage is to remove noise from the input image and enhance the contrast of the images. For noise reduction, a Bilateral Filter is utilized first, followed by enhancement utilizing Contrast Limited Adaptive Histogram Equalization with an unsharp technique. Then Thick Blood vessels are segmented from the enhanced image using the Extended Iterative Self-Organizing clustering (EISOC) Method. From the segmented image, GLCM features are extracted and then features are selected using the Improved Fractional-Order Darwinian Particle Swarm Optimization (IFODPSO) technique. Finally, a Deep CNN classifier is used which classifies the image as Diabetic Retinopathy (DR) or Normal case. Using IFODPSO with a DCNN classifier, 96.6% of the predictions are correct, 3.4% are wrong and the parameter such as Sensitivity is 92.5%, Specificity is 98.9%, Precision is 95.9% values are obtained. By way of classifier efficiency estimation, IFODPSO with DCNN classifier is higher than most other standard classifiers in the literature. [ABSTRACT FROM AUTHOR]

The early diagnosis of diabetic retinopathy (DR) can effectively prevent irreversible vision loss and assist ophthalmologists in providing timely and accurate treatment plans. However, the existing methods based on deep learning have a weak perception ability of different scale information in retinal fundus images, and the segmentation capability of subtle lesions is also insufficient. This paper aims to address these issues and proposes MLNet for DR lesion segmentation, which mainly consists of the Multi-Scale Attention Block (MSAB) and the Lesion Perception Block (LPB). The MSAB is designed to capture multi-scale lesion features in fundus images, while the LPB perceives subtle lesions in depth. In addition, a novel loss function with tailored lesion weight is designed to reduce the influence of imbalanced datasets on the algorithm. The performance comparison between MLNet and other state-of-the-art methods is carried out in the DDR dataset and DIARETDB1 dataset, and MLNet achieves the best results of 51.81% mAUPR, 49.85% mDice, and 37.19% mIoU in the DDR dataset, and 67.16% mAUPR and 61.82% mDice in the DIARETDB1 dataset. The generalization experiment of MLNet in the IDRiD dataset achieves 59.54% mAUPR, which is the best among other methods. The results show that MLNet has outstanding DR lesion segmentation ability. [ABSTRACT FROM AUTHOR]

Background: The retinal pigment epithelium (RPE) is essential for retinal homeostasis. Comprehensively exploring the transcriptional patterns of diabetic human RPE promotes the understanding of diabetic retinopathy (DR). Methods and Results: A total of 4125 differentially expressed genes (DEGs) were screened out from the human primary RPE cells subjected to prolonged high glucose (HG). The subsequent bioinformatics analysis is divided into 3 steps. In Step 1, 21 genes were revealed by intersecting the enriched genes from the KEGG, WIKI, and Reactome databases. In Step 2, WGCNA was applied and intersected with the DEGs. Further intersection based on the enrichments with the GO biological processes, GO cellular components, and GO molecular functions databases screened out 12 candidate genes. In Step 3, 13 genes were found to be simultaneously up-regulated in the DEGs and a GEO dataset involving human diabetic retinal tissues. VEGFA and ERN1 were the 2 starred genes finally screened out by overlapping the 3 Steps. Conclusion: In this study, multiple genes were identified as crucial in the pathological process of RPE under protracted HG, providing potential candidates for future researches on DR. The current study highlights the importance of RPE in DR pathogenesis. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a complication of diabetes that damages the retina and can cause blindness if untreated due to high blood sugar levels. To accurately diagnose and grade DR, it is important to identify retinal lacerations or biomarkers. Optical coherence tomography (OCT) imaging is a commonly used tool by ophthalmologists due to its detailed visualisation of retinal lacerations, which aids in the precise treatment of retinal abnormalities. However, the number of scans obtained daily exceeds the ophthalmologist's capacity to meaningfully analyse them, given the wide range of severe OCT applications and the prevalence of visual disorders. In the past, several research studies have attempted to address this issue using OCT scans. However, none of them have tried to simultaneously perform retinal laceration segmentation and DR grading. To address this problem, we have proposed a new architecture—a cutting-edge decoupled convolutional network consisting of three distinct modules that work together to achieve accurate DR grading based on clinical standards aided by retinal laceration segmentation. Our proposed paper introduces a deep learning framework that leverages dual guidance to improve performance on two related tasks. It was extensively tested using 26,841 multi-vendor scans, four publicly available datasets, and a real-time dataset containing 307 OCT scans from various patients. The results confirmed the effectiveness of our design, with a mean Dice score of 0.88 (4.76% improvement) in retinal laceration segmentation and 98.93% accuracy in DR grading, with an actual positive rate of about 98.46% and a true negative rate of 99.37%. [ABSTRACT FROM AUTHOR]

Objective: Age-related macular degeneration (AMD), particularly its exudative form, is a primary cause of vision impairment in older adults. As diabetes becomes increasingly prevalent in aging, it is crucial to explore the potential relationship between diabetic retinopathy (DR) and AMD. This study aimed to assess the risk of developing overall, non-exudative, and exudative AMD in individuals with DR compared to those without retinopathy (non-DR) based on a nationwide population study in Taiwan. Methods: A retrospective cohort study was conducted using the Taiwan National Health Insurance Database (NHIRD) (2000–2013). A total of 3413 patients were placed in the study group (DR) and 13,652 in the control group (non-DR) for analysis. Kaplan–Meier analysis and the Cox proportional hazards model were used to calculate the hazard ratios (HRs) and adjusted hazard ratios (aHRs) for the development of AMD, adjusting for confounding factors, such as age, sex, and comorbid conditions. Results: Kaplan–Meier survival analysis indicated a significantly higher cumulative incidence of AMD in the DR group compared to the non-DR group (log-rank test, p < 0.001). Adjusted analyses revealed that individuals with DR faced a greater risk of overall AMD, with an aHR of 3.50 (95% CI = 3.10–3.95). For senile (unspecified) AMD, the aHR was 3.45 (95% CI = 3.04–3.92); for non-exudative senile AMD, it was 2.92 (95% CI = 2.08–4.09); and for exudative AMD, the aHR was 3.92 (95% CI = 2.51–6.14). Conclusion: DR is a significant risk factor for both overall, senile, exudative, and non-exudative AMD, even after adjusting for demographic and comorbid conditions. DR patients tend to have a higher prevalence of vascular comorbidities; however, our findings indicate that the ocular pathologies inherent to DR might have a more significant impact on the progression to AMD. Early detection and appropriate treatment of AMD is critically important among DR patients. [ABSTRACT FROM AUTHOR]

Nowadays high-rated and active research in the ophthalmic field has paved the way for the detection of several retinal disorders which helps the ophthalmologist, in scheduling and executing timely treatment. Due to more number of retinal defective patients and less number of medical professionals regular screening is one the most difficult tasks. The primary objective of this research work is to help doctors and other medical professionals predict the diabetic retinopathy. Here in this research article work retina fundus images are taken from the both Public dataset and the in-house clinical dataset from Chaithanya Eye Hospital Kerala. The first stage is to remove noise from the input image and enhance the contrast of the images. For noise reduction, a Bilateral Filter is utilized first, followed by enhancement utilizing Contrast Limited Adaptive Histogram Equalization with an unsharp technique. Then Thick Blood vessels are segmented from the enhanced image using the Extended Adaptive Density-Based Spatial Clustering (EADBSC) Method. The segmented image is fed to the classifier such as Ensemble CNN & Improved dilated ensemble CNN classification, which classifies the image as Diabetic Retinopathy (DR) or Normal case. Using Ensemble CNN, an accuracy of 97.46% is obtained. Improved dilated ensemble CNN-based classifier has an accuracy of 99.19%. By way of their efficiency estimation, the improved dilated ensemble CNN classifier is higher than most standard classifiers. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose Diabetic Retinopathy at an early stage. [ABSTRACT FROM AUTHOR]

Human vision depends heavily on retinal tissue. The loss of eyesight may result from infections of the retinal tissues that are treated slowly or do not work at all. Additionally, the diagnosis is susceptible to inaccuracies when a large dataset is involved. Therefore, a fully automated transfer learning approach for diagnosing diabetic retinopathy (DR) is suggested to minimize human intervention while maintaining high classification accuracy. To address this issue, we proposed a transfer learning-based trilateral attention network (TaNet) for the classification. To boost the visual quality of the DR pictures, a contrast constrained adaptive histogram equalization approach is applied. The preprocessed pictures are then segmented using a bilateral segmentation network (BiSeNet). The BiSeNet segmented the optic disc and blood vessels individually. After the completion of segmentation, the features are extracted. Feature extraction is based on the wavelet scattering transformation approach. The results of many trials were evaluated against the Messidor-2, EYEPACS, and APTOS 2019 datasets. The proposed model was created using a refined pre-trained technique and transfer learning methodology. Finally, the suggested framework was tested using efficiency assessment methods, and the classification rate was recorded as having above 98% sensitivity, specificity, precision, and accuracy. The proposed approach yields greater performance and shows enhancement towards the existing approach. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) stands as a prevalent complication of diabetes mellitus, causing damage to the delicate retinal capillaries and potentially leading to visual impairment. While the exact underlying cause of DR remains elusive, compelling research suggests that mitochondrial energy deficiency and the excessive generation of reactive oxygen species (ROS) play pivotal roles in its pathogenesis. Recognizing that controlling hyperglycemia alone fails to reverse the defects in retinal mitochondria induced by diabetes, current strategies seek to restore mitochondrial function as a means of safeguarding against DR. To address this pressing issue, a comprehensive study was undertaken to explore the potential of phosphocreatine (PCr) in bolstering mitochondrial bioenergetics and providing protection against DR via modulation of the JAK2/STAT3 signaling pathway. Employing rat mitochondria and RGC-5 cells, the investigation meticulously assessed the impact of PCr on ROS production, mitochondrial membrane potential, as well as the expression of crucial apoptotic and JAK2/STAT3 signaling pathway proteins, utilizing cutting-edge techniques such as high-resolution respirometry and western blotting. The remarkable outcomes revealed that PCr exerts a profound protective influence against DR by enhancing mitochondrial function and alleviating diabetes-associated symptoms and biochemical markers. Notably, PCr administration resulted in an upregulation of antiapoptotic proteins, concomitant with a downregulation of proapoptotic proteins and the JAK2/STAT3 signaling pathway. These significant findings firmly establish PCr as a potential therapeutic avenue for combating diabetic retinopathy. By augmenting mitochondrial function and exerting antiapoptotic effects via the JAK2/STAT3 signaling pathway, PCr demonstrates promising efficacy both in vivo and in vitro, particularly in counteracting the oxidative stress engendered by hyperglycemia. In summary, our study sheds light on the potential of PCr as an innovative therapeutic strategy for diabetic retinopathy. By bolstering mitochondrial function and exerting protective effects via the modulation of the JAK2/STAT3 signaling pathway, PCr holds immense promise in ameliorating the impact of DR in the face of oxidative stress induced by hyperglycemia. [ABSTRACT FROM AUTHOR]

Diabetes is one of the most common diseases across the world. It affects numerous parts of our body. Diabetic Retinopathy has an effect on retina which causes Diabetes Mellitus (DM) and it may even lead to blindness. Hence, detecting Diabetic Retinopathy (DR) is important during the early stages of diabetes which can prevent the patients from blindness. The publically accessible dataset of Diabetic Retinopathy (DR) contains numerous images of the retina and its results on Diabetic Retinopathy (DR). Our proposed ideology is to classify the images of the retina using an optimized convolutional neural network (OpCoNet) to detect whether the Diabetic Retinopathy (DR) is proliferative or severe or moderate or mild or normal. The optimized convolutional neural network has enhanced feature extraction and classification mechanism. Gray wolf optimization is used to train the CNN layers. The tested model is compared with the existing methodologies used for the detection of Diabetic Retinopathy (DR). The proposed technique effectually provides an accuracy of 98% and sensitivity of 98.5%. The automatic detection of Diabetic Retinopathy (DR) efficaciously proved in the screening process as well as lessens the trouble on medical services support. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a leading cause of irreversible vision loss in working-age populations. Fat mass and obesity-associated protein (FTO) is an N6-methyladenosine (m6A) demethylase that demethylates RNAs involved in energy homeostasis, though its influence on DR is not well studied. Herein, we detected elevated FTO expression in vitreous fibrovascular membranes of patients with proliferative DR. FTO promoted cell cycle progression and tip cell formation of endothelial cells (ECs) to facilitate angiogenesis in vitro, in mice, and in zebrafish. FTO also regulated EC-pericyte crosstalk to trigger diabetic microvascular leakage, and mediated EC–microglia interactions to induce retinal inflammation and neurodegeneration in vivo and in vitro. Mechanistically, FTO affected EC features via modulating CDK2 mRNA stability in an m6A-YTHDF2-dependent manner. FTO up-regulation under diabetic conditions was driven by lactate-mediated histone lactylation. FB23-2, an inhibitor to FTO's m6A demethylase activity, suppressed angiogenic phenotypes in vitro. To allow for systemic administration, we developed a nanoplatform encapsulating FB23-2 and confirmed its targeting and therapeutic efficiency in mice. Collectively, our study demonstrates that FTO is important for EC function and retinal homeostasis in DR, and warrants further investigation as a therapeutic target for DR patients. Synopsis: The fat mass and obesity-associated (FTO) protein, an N6-methyladenosine (m6A) demethylase, influences endothelial cell (EC) function and retinal homeostasis in diabetic retinopathy (DR), thus providing a promising nanotherapeutic approach for DR. FTO expression is elevated in diabetic mice retinas and vitreous fibrovascular membranes of patients with proliferative DR. FTO triggers diabetes-induced microvascular dysfunction by facilitating neovascularization and regulating EC–pericyte/microglia crosstalk. FTO regulates CDK2 mRNA stability with the YTHDF2 reader in an m6A-dependent manner. Lactic acid regulates FTO expression via histone lactylation. FB23-2 suppresses demethylation activity of FTO to inhibit diabetes-induced EC phenotypes, showing therapeutic potential in DR in mice. The fat mass and obesity-associated (FTO) protein, an N6-methyladenosine (m6A) demethylase, influences endothelial cell (EC) function and retinal homeostasis in diabetic retinopathy (DR), thus providing a promising nanotherapeutic approach for DR. [ABSTRACT FROM AUTHOR]

Diabetics have blood sugar levels that are abnormal. Because of this, their eyesight can be affected. Untreated diabetes has the potential to result in permanent blindness, a grave complication. Among the severe consequences, early detection stands out as a critical hurdle for ensuring effective treatment. Regrettably, accurately assessing fundus images relies on human expertise to determine the stage of diabetic retinopathy. A simplified detection process could greatly benefit a vast population. Leveraging convolutional neural networks (CNN), which have demonstrated efficacy across various domains, diabetic retinopathy identification has seen advancements. The possibility of preventing complete, irreversible blindness lies in timely identification, underscoring the need for a dependable screening mechanism. To enhance the efficiency of screening and reduce errors that plague current models, a novel system has been introduced, capable of categorizing distinct diabetic retinopathy stages. This study focuses on examining datasets of Indian diabetic retinopathy images, encompassing four classes: Microaneurysms (MA), Soft Exudates (SE), Hard Exudates (EX), and Hemorrhages (HE). By employing the proposed VGG16 architecture for feature extraction and the Logistic Regression classifier for classification, an impressive accuracy of 90.4% was achieved. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN) for classification. We tested ourmodel on the IDRiD dataset and achieved an accuracy of 98.06%, a sensitivity of 83.67%, and a specificity of 100% for DR detection and evaluation tests, respectively. Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a leading cause of vision complications and partially sighted which pose considerable diagnostic difficulties because of its diverse and varying symptoms. Some of them include the fact that the disease displays a non-uniform pattern, where patients present different symptoms; the requirement of highly qualified specialists to interpret the images of the fundus; the risk of errors in the interpretation of images or their inconsistency; and the absence of clear morphological signs often makes early diagnosis unlikely. Traditional diagnosis mostly rely on the expert interpretation of retinal images, which can lead to bias and inaccuracy; highlighting the need for improved diagnostic methods. Although traditional Artificial Intelligence (AI) methods enhance the diagnostic capabilities remarkably, their black box nature and information opacity restrict healthcare providers to comprehend the reasoning framework of the AI to build trust and optimize its usage in practice. Explainable AI (XAI) is an emerging approach that addresses the black-box problem by improving the interpretability of models, which allows users to understand the logic behind certain decisions. This research proposed a diagnosis model for detecting diabetic retinopathy using XAI approaches that increases the interpretability of the models to help clinicians understand the reasons behind the decisions. The proposed model is used to enhance diagnostic accuracy, offer comprehensible, and concise insights regarding the diagnostics. The convergence history plots of the proposed model validate the learning process to achieve 94% better diagnostic accuracy than traditional methods while improving interpretability and applicability in healthcare settings, indicating improvement in accuracy and loss reduction.

Abstract Diabetic retinopathy (DR) is a leading cause of irreversible vision loss in working-age populations. Fat mass and obesity-associated protein (FTO) is an N6-methyladenosine (m6A) demethylase that demethylates RNAs involved in energy homeostasis, though its influence on DR is not well studied. Herein, we detected elevated FTO expression in vitreous fibrovascular membranes of patients with proliferative DR. FTO promoted cell cycle progression and tip cell formation of endothelial cells (ECs) to facilitate angiogenesis in vitro, in mice, and in zebrafish. FTO also regulated EC-pericyte crosstalk to trigger diabetic microvascular leakage, and mediated EC–microglia interactions to induce retinal inflammation and neurodegeneration in vivo and in vitro. Mechanistically, FTO affected EC features via modulating CDK2 mRNA stability in an m6A-YTHDF2-dependent manner. FTO up-regulation under diabetic conditions was driven by lactate-mediated histone lactylation. FB23-2, an inhibitor to FTO’s m6A demethylase activity, suppressed angiogenic phenotypes in vitro. To allow for systemic administration, we developed a nanoplatform encapsulating FB23-2 and confirmed its targeting and therapeutic efficiency in mice. Collectively, our study demonstrates that FTO is important for EC function and retinal homeostasis in DR, and warrants further investigation as a therapeutic target for DR patients.

Diabetic retinopathy (DR) is one of the most frequently occurring diabetic complications associated with inflammation and oxidative stress. Platycodin D (PLD) is a bio‐active saponin that has been reported to exhibit anti‐inflammation, anti‐oxidative, and antidiabetic activities. Therefore, we speculated the protective effects of PLD on DR in the present study. Our results demonstrated that PLD attenuated high glucose (HG)‐induced inflammation, as evidenced by decreased production of TNF‐α, IL‐1β, IL‐6. The HG‐induced oxidative stress was prevented by PLD with decreased ROS production and malondialdehyde (MDA) level, as well as increased activities of superoxide dismutase and glutathione (GSH). In addition, treatment of PLD significantly decreased the apoptotic rate in HG‐induced ARPE‐19 cells. The HG‐caused increases in expression of bax and cleaved capsase‐3, as well a decrease in bcl‐2 expression were attenuated by PLD. Furthermore, PLD suppressed the activation of TLR4/MyD88/NF‐κB and enhanced the activation of Nrf2/HO‐1 pathway in HG‐induced ARPE‐19 cells. Additionally, overexpression of TLR4 attenuated the anti‐inflammatory, while knockdown of Nrf2 reversed the anti‐oxidative and anti‐apoptotic activities of PLD in HG‐stimulated ARPE‐19 cells. Furthermore, PLD attenuates retinal damage in DR rats. Finally, we demonstrated that PLD weakened the TLR4/MyD88/NF‐κB p65 pathway and promoted the Nrf2/HO‐1 pathway in vivo. Taken together, these findings indicated that PLD exerted protective effects against DR, which were attributed to the regulation of TLR4/MyD88/NF‐κB and Nrf2/HO‐1 signaling pathways. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR), which is a leading cause of adult blindness, primarily affects individuals with diabetes. The manual diagnosis of DR, with the assistance of an ophthalmologist, has proven to be a time-consuming and challenging process. Late detection of DR is a significant factor contributing to the progression of the disease. To address this issue, the present study utilizes deep learning (DL) and transfer learning algorithms to analyze different stages of DR and precisely detect the condition. Using a large dataset comprising approximately 60,000 images, this study employs ResNet-101, DenseNet121, InceptionResNetV2, and EfficientNetB0 DL models to automatically assess the progression of DR. Images of patients' eyes are inputted into the models, and the DL architectures are adapted to extract relevant features from the eye images. The study's findings demonstrate that DenseNet121 outperforms ResNet-101, InceptionResNetV2, and EfficientNetB0 in accurately classifying the five stages of DR. The accuracy of the models was 97%, 96%, 95%, and 94%, respectively. These results underscore the effectiveness of DL in achieving an accurate and comprehensive classification of retinitis pigmentosa. By enabling accurate and timely diagnosis of DR, the application of DL techniques significantly contributes to the field of ophthalmology, facilitating improved treatment decisions for patients. [ABSTRACT FROM AUTHOR]

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution. This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature. The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a chronic eye disease that is common in people who have had diabetes for a long time. If the disease is not treated during the early stages, it leads to complete vision loss, which can be avoided by treating in the early stages. The development of effective tools is critical for carrying out large-scale diagnostics at low cost while avoiding human bias. In this work, we propose a self-adaptive ensemble approach for retinopathy severity grading by stacking multiple dual attention based approaches. The proposed dual attention model leverages two distinct attention mechanisms. The model can focus on lesion-specific regions with the first level of attention, while the second level of attention allows it to learn correlations between spatial descriptors. The proposed model effectively predicts the severity level of retinopathy with dual levels of attention. We also present a self-adaptive meta learner for effectively stacking multiple dual attention models. Experimental studies on the benchmark APTOS 2019 dataset reveals that the proposed approach outperforms several existing models by achieving an accuracy of 86.22%. The proposed model exhibits generalization not only in terms of accuracy but also in terms of other evaluation measures, and achieves a quadratic kappa score of 89.65% and an AUC score of 96.47%. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy is a scenario in medical field which leads to the rise of damage of blood vessels in the retina which is due to diabetes mellitus. The suitable detection for this kind of problems and care to be done immediately in order to prohibit loss of sight in a person. Presently, diagnosing Diabetic Retinopathy manually is a time-consuming process where they require experienced clinicians to examine the digital-colored fundus images. Here, we have proposed a machine learning technology using Convolutional Neural Network (CNN) approach which has emerged as an operative productive tool in medical image examination for the classification and detection of Diabetic Retinopathy (DR) in real-world. The different layers which are used to detect the brain tumor are conv2D, Activation, MaxPooling2D, Dense and Flatten. The set used here considers 750 retinal images, with 600 training images and the test set considers 150 images with the accuracy of 82.75% which ran for 80 epochs. [ABSTRACT FROM AUTHOR]

In medical applications, ocular OCT (optical coherence tomography) is used to assess glaucoma, macular degeneration, diabetic macular edema and other eye diseases as it is capable of showing the cross-sections of tissue layers. The creation of new blood vessels in the choroid layer of the eye is known as choroidal neovascularization (CNV). The aging and macular degeneration will represent the symptom DRUSEN. Our sharp central vision is affected due to DRUSEN. An irreversible vision loss is caused in diabetic patients due to diabetic macular edema (DME). It is mainly due to the leaking of blood vessels in the retina. This research work focus on designing a clinical decision support system to assist the ophthalmologist in classifying the three different types of eye diseases. The existing nine pre-trained CNN models are used for this purpose. The extracted features are used to generate the trained model that is further used for eye disease classification. The training accuracy, validation accuracy, training loss and validation loss are computed for 100 iterations for each pre-trained CNN models during training and validation. The trained model obtained after training is used as input to the classifier, which classifies the images under-diagnosis into NORMAL (normal eye), CNV, DME and DRUSEN. The performance metrics of the classifier designed using each pre-trained models are evaluated and compared for four classes independently. The test results show that the performance of the classifier implemented using the pre-trained model InceptionV3 is better than all other models. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to compare posterior subtenon triamcinolone acetonide injection either formulated or alone versus suprachoroidal triamcinolone in the management of diabetic macular edema. Methods: This study is a prospective interventional study that included 75 patients, divided into three groups, each group with 25 patients. Group I received a combination of triamcinolone acetonide (TA) (40 mg) and VISCOAT, which is a combination of sodium chondroitin sulfate (20 mg) and sodium hyaluronate (15 mg). The injection was done in the posterior subtenon space using the NAGATA cannula. Group II received TA (40 mg) in the posterior subtenon space. Group III underwent an injection of 4 mg/100µl of TA in the supra choroidal space. Results: We found a statistically significant difference between the three studied groups regarding BCVA (P = 0.001) and CMT at six months postoperative (P = 0.001) with the highest median BCVA and lowest median CMT observed in the formulated TA group. Conclusion: We concluded that early treatment of DME by formulated TA is better than TA alone, and suprachoroidal TA in the form of increasing the BCVA and decreasing the CMT without any elevation of IOP. Trial registration number NCT05464953. Date of registration 17/7/2022 (retrospectively registered). [ABSTRACT FROM AUTHOR]

The escalating prevalence of diabetes globally, exacerbated by lifestyle changes postpandemic-- including increased screen time, sedentary behavior, and remote work-- has consequently driven a surge in associated complications, notably, Diabetic Retinopathy (DR). This ocular complication presents a pressing concern due to its potential to precipitate irreversible vision loss. Consequently, the necessity for timely and accurate DR detection is paramount, especially in circumstances where conventional diagnostic approaches are either challenging or financially prohibitive. Capitalizing on the prowess of fuzzy logic in managing uncertainties, this study introduces an innovative application of Extended Fuzzy Logic for the early-stage detection of DR. Rather than focusing solely on overt symptoms, this approach discerns subtle similarities in retinal irregularities between diabetic patients and non-diabetic individuals. To quantify these similarities, the 'f-validity' value was computed based on DR risk factors and associated symptoms, which were subsequently transformed into membership function values. The aggregation of these values was facilitated by the Ordered Weighted Averaging (OWA) operator. The experimental outcomes of this approach align satisfactorily with expert anticipations, boasting an accuracy of 90%, a precision of 92.2%, and a sensitivity of 75%. These results, when juxtaposed against contemporary studies in the field, underscore the promise of this scheme in advancing early diagnostics of DR. The study thus proposes a potential solution that leverages the power of fuzzy logic to address the burgeoning challenge of DR. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a chronic complication of diabetes that damages the retinal blood vessels, leading to impaired vision and even blindness, and is one of the top three eye diseases causing human blindness. An effective DR grading algorithm can help ophthalmologists to diagnose patients and improve efficiency. Therefore, we propose a fusion network based on transformer and convolutional neural network (CNN) to perform DR grading. The proposed approach addresses two critical issues in the task: i) The CNN-based DR classification method has a small receptive field, so the range of available information is limited. The Transformer-based DR classification method has a large receptive field, but it is easy to lose local details; ii) Existing approaches treat DR grading as a traditional multiclass classification task, which ignores ordinal information between DR of different levels. To address i), we fuse the multi-level features of CNN and Transformer at different stages, and realize the enhancement and interaction of local and global information through the fusion module. To address ii), we propose a KC loss by formulating DR grading as a joint ordinal regression and multiclass classification problem, and obtain both category-supervised information and ordinal supervised information. Extensive qualitative and quantitative assessments have shown that our approach achieves superior performance on publicly available DeepDR and IDRiD datasets. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal surface. The latest advancement in this field, OCT angiography (OCTA), takes this to the next level by providing detailed vascular information without requiring dye injections. One of the most significant indicators of DR and AMD is neovascularization, the abnormal growth of unhealthy vessels. In this work, the techniques and algorithms used for the automatic detection, classification, and segmentation of neovascularization in OCTA images are explored. From image processing to machine learning and deep learning, works related to automated image analysis of neovascularization are summarized from different points of view. The problems and future work of each method are also discussed. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a condition that affects the retina and causes vision loss due to blood vessel destruction. The retina is the layer of the eye responsible for visual processing and nerve signaling. Diabetic retinopathy causes vision loss, floaters, and sometimes blindness; however, it often shows no warning signals in the early stages. Deep learning-based techniques have emerged as viable options for automated illness classification as large-scale medical imaging datasets have become more widely available. To adapt to medical image analysis tasks, transfer learning makes use of pre-trained models to extract high-level characteristics from natural images. In this research, an intelligent recommendation-based fine-tuned EfficientNetB0 model has been proposed for quick and precise assessment for the diagnosis of diabetic retinopathy from fundus images, which will help ophthalmologists in early diagnosis and detection. The proposed EfficientNetB0 model is compared with three transfer learning-based models, namely, ResNet152, VGG16, and DenseNet169. The experimental work is carried out using publicly available datasets from Kaggle consisting of 3,200 fundus images. Out of all the transfer learning models, the EfficientNetB0 model has outperformed with an accuracy of 0.91, followed by DenseNet169 with an accuracy of 0.90. In comparison to other approaches, the proposed intelligent recommendation-based fine-tuned EfficientNetB0 approach delivers state-of-the-art performance on the accuracy, recall, precision, and F1-score criteria. The system aims to assist ophthalmologists in early detection, potentially alleviating the burden on healthcare units.