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Widespread eye conditions such as cataracts, diabetic retinopathy, and glaucoma impact people worldwide. Ophthalmology uses fundus photography for diagnosing these retinal disorders, but fundus images are prone to image quality challenges. Accurate diagnosis hinges on high-quality fundus images. Therefore, there is a need for image quality assessment methods to evaluate fundus images before diagnosis. Consequently, this paper introduces a deep learning model tailored for fundus images that supports large images. Our division method centres on preserving the original image's high-resolution features while maintaining low computing and high accuracy. The proposed approach encompasses two fundamental components: an autoencoder model for input image reconstruction and image classification to classify the image quality based on the latent features extracted by the autoencoder, all performed at the original image size, without alteration, before reassembly for decoding networks. Through post hoc interpretability methods, we verified that our model focuses on key elements of fundus image quality. Additionally, an intrinsic interpretability module has been designed into the network that allows decomposing class scores into underlying concepts quality such as brightness or presence of anatomical structures. Experimental results in our model with EyeQ, a fundus image dataset with three categories (Good, Usable, and Rejected) demonstrate that our approach produces competitive outcomes compared to other deep learning-based methods with an overall accuracy of 0.9066, a precision of 0.8843, a recall of 0.8905, and an impressive F1-score of 0.8868. The code is publicly available at https://github.com/saifalkhaldiurv/VISTA_-Image-Quality-Assessment. [ABSTRACT FROM AUTHOR]

Purpose: We designed a dual-modal fusion network to detect glaucomatous optic neuropathy, which utilized both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images. Methods: A total of 327 healthy subjects (410 eyes) and 87 glaucomatous optic neuropathy patients (113 eyes) were included. The retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images were used as predictors in the dual-modal fusion network to diagnose glaucoma. The area under the receiver operation characteristic curve, accuracy, sensitivity, and specificity were measured to compare our method and other approaches. Results: The accuracy of our dual-modal fusion network using both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images was 0.935 and we achieved a significant larger area under the receiver operation characteristic curve of our method with 0.968 (95% confidence interval, 0.937–0.999). For only using retinal nerve fiber layer thickness, we compared the area under the receiver operation characteristic curves between our network and other three approaches: 0.916 (95% confidence interval, 0.855, 0.977) with our optical coherence tomography Net; 0.841 (95% confidence interval, 0.749, 0.933) with Clock sectors division; 0.862 (95% confidence interval, 0.757, 0.968) with inferior, superior, nasal temporal sectors division and 0.886 (95% confidence interval, 0.815, 0.957) with optic disc sectors division. For only using fundus images, we compared the area under the receiver operation characteristic curves between our network and other two approaches: 0.867 (95% confidence interval: 0.781–0.952) with our Image Net; 0.774 (95% confidence interval: 0.670, 0.878) with ResNet50; 0.747 (95% confidence interval: 0.628, 0.866) with VGG16. Conclusion: Our dual-modal fusion network utilizing both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images can diagnose glaucoma with a much better performance than the current approaches based on optical coherence tomography only or fundus images only. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to examine the relationship between the morphological parameters of retinal arteriolar bifurcations and coronary artery disease (CAD). Methods: In this cross-sectional observational study, fundus photography was conducted on 444 participants to capture retinal arteriolar bifurcations. A total of 731 fundus photographs yielded 9625 measurable bifurcations. Analyzed bifurcation parameters included the diameters of the parent vessel (d0), the larger branch (d1), and the smaller branch (d2), as well as the angles (θ1) and (θ2) representing the orientation of each branch in relation to the parent vessel, respectively. Additionally, theoretical optimal angles ( θ 1 ′ ) and ( θ 2 ′ ), calculated from the measured parameters, provided a benchmark for ideal bifurcation geometry. The study assessed the variation in these parameters across different levels of coronary atherosclerosis severity. Results: After adjusting for anatomical characteristics including the asymmetry ratio, area ratio, and distance to the optic disc, we observed that patients with severe coronary artery stenosis had significant deviations from the theoretical optimal bifurcation angles, with a decrease in ( θ 1 ′ ) and an increase in ( θ 2 ′ ) compared to those with moderate stenosis. Conclusion: The findings suggest a clear alteration in retinal arteriolar bifurcation morphology among patients with severe CAD, which could potentially serve as an indicator of disease severity. [ABSTRACT FROM AUTHOR]

Early diagnosis is crucial to prevent a diabetic patient from being affected by blindness. Automatic and accurate detection of diabetic retinopathy is essential. A methodology for the detection and classification of diabetic retinopathy is presented in this paper. Data preprocessing methods are used to highlight subtle information to classify DR anomalies accurately. Image-enhancing techniques are used to boost image quality. Following the preprocessing stage, three main procedures are performed: segmentation, feature extraction, and classification. In contrast to brute force methods, metaheuristic algorithms can explore the solution space more quickly and provide precise, ideal solutions. Due to a lack of detailed image data, it is impossible to determine the precise limits based on image segmentation features. Threshold segmentation is the most effective choice for segmenting fundus images since it has benefits, including simple implementation, low computational complexity, and improved performance. A new variant of grasshopper optimization is proposed using the multi-thresholding version. The segmentation using the proposed model gives high accuracy, even for tiny lesions. A total of 41 features were extracted from the segmented fundus images. Finally, the improved Naïve Bayes classifier classifies the various classes of diabetic retinopathy. The proposed methodology was trained and tested over the DIARETDB0, Messidor-2, Eye pacs-1, and APTOS datasets. The improved naive Bayes classifier enhanced the classification of diabetic retinopathy by an accuracy of 99.98% on the APTOS dataset, which was better than the previously existing techniques. The results proved that the improved naive Bayes classifier adequately diagnoses diabetic retinopathy from the retinal fundus images. [ABSTRACT FROM AUTHOR]

Abstract Introduction The aim of this study was to examine the relationship between the morphological parameters of retinal arteriolar bifurcations and coronary artery disease (CAD). Methods In this cross-sectional observational study, fundus photography was conducted on 444 participants to capture retinal arteriolar bifurcations. A total of 731 fundus photographs yielded 9625 measurable bifurcations. Analyzed bifurcation parameters included the diameters of the parent vessel (d 0), the larger branch (d 1), and the smaller branch (d 2), as well as the angles (θ 1) and (θ 2) representing the orientation of each branch in relation to the parent vessel, respectively. Additionally, theoretical optimal angles ( $$\theta_{1}^{\prime}$$ θ 1 ′ ) and ( $$\theta_{2}^{\prime}$$ θ 2 ′ ), calculated from the measured parameters, provided a benchmark for ideal bifurcation geometry. The study assessed the variation in these parameters across different levels of coronary atherosclerosis severity. Results After adjusting for anatomical characteristics including the asymmetry ratio, area ratio, and distance to the optic disc, we observed that patients with severe coronary artery stenosis had significant deviations from the theoretical optimal bifurcation angles, with a decrease in ( $$\theta_{1}^{\prime}$$ θ 1 ′ ) and an increase in ( $$\theta_{2}^{\prime}$$ θ 2 ′ ) compared to those with moderate stenosis. Conclusion The findings suggest a clear alteration in retinal arteriolar bifurcation morphology among patients with severe CAD, which could potentially serve as an indicator of disease severity.

AIM:To explore the fundus vascular characteristics of healthy individuals based on deep learning techniques, with a view to discovering the range of normal values of the fundus arteries and veins, as well as the relationship between physiological factors, such as gender, age, body mass index(BMI), blood pressure, and fundus vasculature characteristics.METHODS:Fundus images of healthy people were taken from a professional fundus camera, and the subject's blood pressure and laboratory test was collected. Additionally, the fundus arteries and veins were segmented by the improved U-Net model, and the color, morphology and Haralick texture features of the vessels were extracted from computer vision technology.RESULTS:A total of 4 487 cases fundus images were taken and 326 cases with healthy and clear fundus images were screened, including 200 males and 126 females. There were differences in the morphology, color, and textural characteristics of the left and right eyes, as well as of the fundus arterioles and veins, with a mean vessel width(width)of 1.146 in the arteries and 1.430 in the veins, and an arteriovenous ratio about 4:5. Fundus artery and vein characteristics in healthy individuals of different ages(21-30, 31-40, 41-50): compared with the healthy population aged 21-30 and 31-40 years, arterial and venous inverse difference moment(idm), f12 and venous angular second moment(asm)values increased, and arterial and venous contrast(con), entropy(ent), difference entropy(den), and venous sum entropy(sen)values decreased in 41-50 years. Compared with the 21-30 years age group, arterial f12 values increased and venous con values decreased in 31-40 years(all P0.05). Fundus characteristics of healthy people with different degrees of blood pressure: there were statistically significant differences in fundus arteriosus area, width, and venous con, idm, dva, and den values between the normal blood pressure and high blood pressure groups(all P

Abstract Recent advancements in artificial intelligence (AI) have prompted researchers to expand into the field of oculomics; the association between the retina and systemic health. Unlike conventional AI models trained on well-recognized retinal features, the retinal phenotypes that most oculomics models use are more subtle. Consequently, applying conventional tools, such as saliency maps, to understand how oculomics models arrive at their inference is problematic and open to bias. We hypothesized that neuron activation patterns (NAPs) could be an alternative way to interpret oculomics models, but currently, most existing implementations focus on failure diagnosis. In this study, we designed a novel NAP framework to interpret an oculomics model. We then applied our framework to an AI model predicting systolic blood pressure from fundus images in the United Kingdom Biobank dataset. We found that the NAP generated from our framework was correlated to the clinically relevant endpoint of cardiovascular risk. Our NAP was also able to discern two biologically distinct groups among participants who were assigned the same predicted systolic blood pressure. These results demonstrate the feasibility of our proposed NAP framework for gaining deeper insights into the functioning of oculomics models. Further work is required to validate these results on external datasets.

Recent advancements in artificial intelligence (AI) have prompted researchers to expand into the field of oculomics; the association between the retina and systemic health. Unlike conventional AI models trained on well-recognized retinal features, the retinal phenotypes that most oculomics models use are more subtle. Consequently, applying conventional tools, such as saliency maps, to understand how oculomics models arrive at their inference is problematic and open to bias. We hypothesized that neuron activation patterns (NAPs) could be an alternative way to interpret oculomics models, but currently, most existing implementations focus on failure diagnosis. In this study, we designed a novel NAP framework to interpret an oculomics model. We then applied our framework to an AI model predicting systolic blood pressure from fundus images in the United Kingdom Biobank dataset. We found that the NAP generated from our framework was correlated to the clinically relevant endpoint of cardiovascular risk. Our NAP was also able to discern two biologically distinct groups among participants who were assigned the same predicted systolic blood pressure. These results demonstrate the feasibility of our proposed NAP framework for gaining deeper insights into the functioning of oculomics models. Further work is required to validate these results on external datasets. [ABSTRACT FROM AUTHOR]

Globally, eye disorders have been a major issue, especially in developing nations where resources for technology and financing are limited. Due to its tremendous feature learning ability, CNN has obtained remarkable progress in the area of fundus images. Through appropriate analysis and examination of fundus images, computer-aided diagnosis may yield information with a standard value for experts in clinical diagnosis or screening. However, the majority of earlier investigations have focused on the identification of a particular fundus disease, and the accurate fast classification of multiple fundus diseases remains a major challenge. Extremely numerous retinal fundus images must be analyzed to achieve a categorization that is reliable, fast, and precise. As a result, this research aims to propose a novel classification model for eye illnesses based on four major steps: (a) Pre-processing, (b) segmentation of blood vessels, (c) Extracting features and (d) Multi-classification of eye disease. In order to accurately classify the images, it is necessary to extract the essential informational characteristics from the segmented blood vessels. The categorization of eye diseases is carried out using a hybrid classifier that integrates the SqueezeNet and Long-Term Recurrent Convolutional Network (LRCN). We trained and validated our models using tenfold cross-validation tests on a database of fundus images including five categories: Normal, Retinitis Pigmentosa, Pathological Myopia, Maculopathy and Glaucoma. The efficacy of the presented approach is evaluated based on specificity, F1-score, recall, precision and accuracy of 98%, 97.8%, 97.6%, 98.4% and 98% respectively. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives: The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology: A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results: Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion: In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening. [ABSTRACT FROM AUTHOR]

Diabetes results in diabetic retinopathy. which occurs due to a change in the vasculature structure. Diabetic abnormalities such as microaneurysms, haemorrhages, and "exudates" describe the stage of diabetic retinopathy. Early-stage detection gains prime importance to avoid permanent sight loss. Exudates are one of the most noticeable signs of diabetic retinopathy. This work presents the automatic and non-invasive approach to detect exudates using requisitely adjusted morphological operators. The use of Standard deviation of histogram equalized image after optic disc detection and deletion is the work's key contribution. The standard deviation offers the tendency of distributing the favourable exudate pixels towards the mean value. The proposed method is implemented on the "IDRiD (Indian Diabetic Retinopathy Image Dataset)". The detected exudates are compared with provided ground-truths reported accuracy of 99.13%. The outcomes prove its effectiveness of the proposed method and can aid ophthalmologists in diagnosis. [ABSTRACT FROM AUTHOR]

Glaucoma is an eye disease which has been one of the leading causes of loss of vision worldwide. It occurs when the fluid pressure in the optic nerve increases which causes the damage to the optic nerve. Detection of glaucoma is necessary for proper and timely treatment. In this paper, an automated system is developed for glaucoma diagnosis in which a two-dimensional empirical wavelet transform (2D-EWT)-based fundus image decomposition method is developed. A novel method is suggested for magnitude spectrum-based image segmentation. In 2D-EWT, the average magnitude spectrum along the horizontal or vertical axis is considered which discards the information along one axis. In the suggested method, the average magnitude spectrum along both axes is utilized and more decomposed images can be obtained. The decomposed fundus images are given to the ResNet-18-based deep learning model for glaucoma identification. The performance of the proposed method is computed in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPEC). The proposed method has achieved the ACC of 99.33% with 99.35% SEN, and 99.25% SPEC which is more than the compared methods. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a complication of diabetes that causes blindness and the early detection of diabetics using retinopathy images remains a challenging task. Hence, a novel, earlier smart prediction of diabetic retinopathy from fundus image under Innovative ResNet Optimization is introduced to effectively detect the earlier stage of DR from Fundus image. Initially, the fundus image is scaled during preprocessing and converted into a grayscale format. As the existing studies neglect some deserving unique features that are crucial for predicting the earliest signs of DR, a novel Fractional Radon Transform with Visibility Graph is introduced for extracting the novel features such as microaneurysms count, dot and blot hemorrhages count, statistical measures, and retinal layer thickness, in which a Generalized Cosine Fractional Radon Transform is used to capture the image's fine-scale texture information thereby effectively capturing the statistical measures, while a weighted Horizontal Visibility Graph is made to examine the apparent spatial relationships between pixel pairs in the image based on the values of the pixels' gray levels. Further, the existing works failed to identify the small fine dark areas that were ignored throughout the morphological opening process. In order to overcome this issue, a Morphological Black Hat Transform with Optimized ResNet Algorithm is implemented, where segmentation is made through Enriched Black Hat Transform-based Morphological operation to identify fine dark regions among the pixels inside the eye samples, and the classification is done by using ResNet-driven S-GOA (Socio Grasshopper Optimization Algorithm), to optimally predict the stages of DR. The result obtained showed that the proposed model outperforms existing techniques with high performance and accuracy. [ABSTRACT FROM AUTHOR]

In ophthalmology, using fundus images to identify ocular diseases early may pose challenges for clinicians. Manually diagnosing ocular conditions is time-consuming, difficult, and requires experimentation. As a result, technology was created to help computers differentiate between ocular diseases. It is possible to create a system of this type due to different learning algorithms based on visual capabilities. Recent breakthroughs in deep learning and machine learning have led to the development of intelligent systems that improve accuracy and efficiency in classifying eye diseases. The purpose of this study is to conduct a comprehensive survey of modern systems that classify ocular problems using different methods, including pre-trained deep learning networks, using the Ocular Disease Intelligent Recognition (ODIR) dataset. The goal is to build and train a model that can recognize and classify ocular disorders. Previous research indicates the increasing use of deep learning techniques. CNN-based methods have spread widely in this field, compared to traditional manual procedures, due to their outstanding results. The most prominent deep learning techniques are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and various learning methods for increasing and transferring data. The survey highlights the potential of these systems to enhance classification accuracy and sensitivity while addressing challenges such as data availability, interpretability, and integration with clinical practice. [ABSTRACT FROM AUTHOR]

BackgroundPathologic myopia (PM) associated with myopic maculopathy (MM) is a significant cause of visual impairment, especially in East Asia, where its prevalence has surged. Early detection and accurate classification of myopia-related fundus lesions are critical for managing PM. Traditional clinical analysis of fundus images is time-consuming and dependent on specialist expertise, driving the need for automated, accurate diagnostic tools.MethodsThis study developed a deep learning-based system for classifying five types of MM using color fundus photographs. Five architectures—ResNet50, EfficientNet-B0, Vision Transformer (ViT), Contrastive Language-Image Pre-Training (CLIP), and RETFound—were utilized. An ensemble learning approach with weighted voting was employed to enhance model performance. The models were trained on a dataset of 2,159 annotated images from Shenzhen Eye Hospital, with performance evaluated using accuracy, sensitivity, specificity, F1-Score, Cohen’s Kappa, and area under the receiver operating characteristic curve (AUC).ResultsThe ensemble model achieved superior performance across all metrics, with an accuracy of 95.4% (95% CI: 93.0–97.0%), sensitivity of 95.4% (95% CI: 86.8–97.5%), specificity of 98.9% (95% CI: 97.1–99.5%), F1-Score of 95.3% (95% CI: 93.2–97.2%), Kappa value of 0.976 (95% CI: 0.957–0.989), and AUC of 0.995 (95% CI: 0.992–0.998). The voting ensemble method demonstrated robustness and high generalization ability in classifying complex lesions, outperforming individual models.ConclusionThe ensemble deep learning system significantly enhances the accuracy and reliability of MM classification. This system holds potential for assisting ophthalmologists in early detection and precise diagnosis, thereby improving patient outcomes. Future work could focus on expanding the dataset, incorporating image quality assessment, and optimizing the ensemble algorithm for better efficiency and broader applicability.

Diabetic retinopathy is manually predicted by ophthalmologists using features such as variations in length and thickness of blood vessels, lesions like hemorrhages, exudes, and microaneurysms in fundus images. However, the images captured by the fundus camera sensor are prone to noise, undergo uneven illumination, and poor contrast. To effectively identify retinal diseases at an earlier stage, it is necessary to eliminate noise and improve image resolution. A hybrid model for denoising and enhancement is proposed that combines a modified morphological filter and a denoising net. The proposed multi-angle two-stage morphological filter and denoising net (MMFDNet) aids the recovery of a clean image and preserves features from the degraded retinal image. This discriminative learning denoising model removes various noise levels of speckle and additive white Gaussian noise (AWGN) from the fundus image. In Denoising Net, the noisy fundus image is restructured using a down-sampling operation, and the four sub-images are combined with a tunable noise level map, which increases the size of the receptive field by improving the speed and performance of the network. The denoised sub-images are grouped to get the latent image, and the morphological filter is used to improve the quality of the latent image. The effectiveness of this method is tested quantitatively and qualitatively with other existing methods. The databases used to analyze the MMFDNet are proprietary dataset, Messidor and STARE dataset. Compared to existing methodologies, the proposed enhancement technique improves peak signal-to-noise ratio (PSNR) by 2.29%, structural similarity index (SSIM) value by 9.53% and reduces the error by mean square error of 16.31% for Gaussian noise with a noise variance of 0.001. Similarly, improves PSNR by 6.60%, SSIM value by 0.33% and reduces the error by MSE of 47.81% for speckle noise with a noise variance of 0.001. The experimental results illustrate that the proposed MMFDNet outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy, an eye complication that causes retinal damage, can impair the vision and even result in blindness, if not treated on time. Regular eye screening is essential for patients with diabetics because diabetic retinopathy advances significantly without symptoms. Exudates are a primary symptom of diabetic retinopathy, and their automatic recognition can help in early diagnosis. The convolution operation which concentrates mostly on extracting the local features provides less emphasis on global information resulting the long-range dependencies to be addressed while traversing through multiple layers. The proposed segmentation model utilizes both the channel and spatial attention mechanisms to effectively establish the long-range dependencies at various levels of feature extraction. The proposed methodology also utilizes the convolutional long- and short-term memory algorithm during the propagation from input-to-state and from the state-to-state to take into account the spatiotemporal dependencies and the residual extended skip block for widening the network's receptive zone. Implementing the potentials of neural networks, this study excels at identifying complex patterns and minute features in retinal images. The effectiveness of the proposed method has been verified by conducting experiments on various retinal image datasets, such as IDRiD, MESSIDOR, DIARETDB0, and DIARETDB1, which clearly indicates the superiority of this method over other existing methods across a wide range of evaluation metrics, namely specificity, F1-score, accuracy, sensitivity, and intersection-over-union. Additionally, the model's ability to achieve an overall accuracy of 97.7% makes it a viable application that can provide clinicians important insights into the diagnosis and treatment of diabetic retinopathy. [ABSTRACT FROM AUTHOR]

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Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, ASNet, Swin-Unet, and TransUNet). [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed. [ABSTRACT FROM AUTHOR]

Segmentation of retinal blood vessels from fundus images is vital to assist ophthalmologists in diagnosing different eye diseases like Arteriosclerosis, Glaucoma, Diabetic Retinopathy, Hypertension, and Choroidal Neovascularization. Accurate detection and timely treatment of these eye diseases can prevent irreversible impairment of vision. In computer-aided automatic diagnosis, Preprocessing of fundus images is necessary to reduce the uneven retinal image illumination, as well as the undesirable effect arising from the contrast differences between the retinal blood vessels and the background. In this study, the Hessian-based Frangi vesselness filter is proposed to enhance vasculature in fundus images. This approach seeks to extract thin vessels to diminish the intensity disparity between thick and thin vessels. Its accuracy has never, however, been thoroughly evaluated. To verify the effectiveness of the suggested filter for boosting vessel-like structures in the fundus images, an experimental approach is presented in this paper. The ability of the filter to improve vessel-like structures in fundus images is validated, and an experimental technique for evaluating filter performance using the U-Net model is described. Five quantitative performances, namely Accuracy, Recall, Specificity, Precision, and F1 Score measures on the DRIVE, HRF, DIARETDB1, DIARETDB0, CHASEDB1, ORIGA, DRISHTI GS, DRIONS DB, FIRE, and FIOT datasets, were evaluated and quantified in these experiments to validate the efficacy of the proposed filter. The results demonstrated a noteworthy improvement by achieving an Accuracy of 99.79 %, 99.84 %, 99.64 %, 99.72 %, 99.57 %, 99.48 %, 99.77 %, 99.05 %, 99.93 %, and 99.40 % respectively. Empirical evidence supports the advantages of this approach.

Background Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.