Microaneurysms and Exudates Detection in Retinal Images using Deep Neural Network

One frequent diabetes consequence that affects the eyes is diabetic retinopathy(DR). The most frequent reasonfor blindness in working-age adults in the world is diabetic retinopathy. One in three diabetics has diabetic retinopathy to some extent. DR affects nearly all persons having type 1 diabetes and more than 60% of people having type 2 diabetes to some extent after 20 years of diabetes. In the US, approximately 4.2 million persons, 40 and older have diabetic retinopathy. In the United States, 12% of all new occurrences of blindness are caused by diabetic retinopathy.A 95% reduction in the risk of blindness is possible with a diabetic retinopathy early detection and treatment. The retina's appearance, the presence or absence of Microaneurysm and Exudates, and the degree of participation are all taken into account in the grading process. It has been demonstrated that deep neural networks (DNNs) work well for automatically grading diabetic retinopathy. The features like Microaneurysm and Exudates that are diagnostic of various stages of diabetic retinopathy are taught to these DNNs utilizing vast datasets of retinal pictures and accompanying grading information.It has been demonstrated that deep neural networks (DNNs) are efficient in automatically detecting diabetic retinopathy from retinal pictures. Sensitivity, specificity, precision, accuracy, and Kappa value are used to measure how well DNNs work in detecting diabetic retinopathy; these values are 95.74%, 92.31%, 96.77%, 94.74%, and 0.87, respectively.

Diabetic retinopathy is the main cause for the loss of vision among working age adults. Therefore, early detection of diabetic retinopathy is vital for the prevention of blindness. Vascular tortuosity (amount of twists and turns) is an early indicator of diabetes and diabetic retinopathy compared to other signs observed in the retinal image. We propose a novel tortuosity index to quantify retinal vascular tortuosity including dilations and elongation due to vascular augmentation caused by diabetes in addition to conventional characteristics; curvature and twists. In the proposed method, the tortuosity of a blood vessel is quantified using the eccentricity of the pixels of its skeleton. Retinal images of 110 voluntary participants (72 healthy subjects, 5 Type I diabetic, 28 Type II diabetic and 5 diabetic retinopathy patients) were acquired using a retinal camera to study the capabilities of the proposed tortuosity index. The derived tortuosity values using the novel method were significantly different among healthy, Type II diabetic and diabetic retinopathy patients (p-value < 0.001). While tortuosity values of diabetic retinopathy patients were significantly higher than all other participants, the healthy group showed less tortuosity than others. When compared to age, diabetes was identified as the predominant factor which causes the increase in retinal vascular tortuosity.

Rapid, bloody, and blinding diabetic retinopathy

Screening for Diabetic Retinopathy in Youth-Onset Diabetes

PurposeTo review the current therapeutic options for the management of diabetic retinopathy (DR) and diabetic macular edema (DME) and examine the evidence for integration of laser and pharmacotherapy.MethodsA review of the PubMed database was performed using the search terms diabetic retinopathy, diabetic macular edema, neovascularization, laser photocoagulation, intravitreal injection, vascular endothelial growth factor (VEGF), vitrectomy, pars plana vitreous surgery, antiangiogenic therapy. With additional cross-referencing, this yielded 835 publications of which 301 were selected based on content and relevance.ResultsMany recent studies have evaluated the pharmacological, laser and surgical therapeutic strategies for the treatment and prevention of DR and DME. Several newer diagnostic systems such as optical coherence tomography (OCT), microperimetry, and multifocal electroretinography (mfERG) are also assisting in further refinements in the staging and classification of DR and DME. Pharmacological therapies for both DR and DME include both systemic and ocular agents. Systemic agents that promote intensive glycemic control, control of dyslipidemia and antagonists of the renin-angiotensin system demonstrate beneficial effects for both DR and DME. Ocular therapies include anti-VEGF agents, corticosteroids and nonsteroidal anti-inflammatory drugs. Laser therapy, both as panretinal and focal or grid applications continue to be employed in management of DR and DME. Refinements in laser devices have yielded more tissue-sparing (subthreshold) modes in which many of the benefits of conventional continuous wave (CW) lasers can be obtained without the adverse side effects. Recent attempts to lessen the burden of anti-VEGF injections by integrating laser therapy have met with mixed results. Increasingly, vitreoretinal surgical techniques are employed for less advanced stages of DR and DME. The development and use of smaller gauge instrumentation and advanced anesthesia agents have been associated with a trend toward earlier surgical intervention for diabetic retinopathy. Several novel drug delivery strategies are currently being examined with the goal of decreasing the therapeutic burden of monthly intravitreal injections. These fall into one of the five categories: non-biodegradable polymeric drug delivery systems, biodegradable polymeric drug delivery systems, nanoparticle-based drug delivery systems, ocular injection devices and with sustained release refillable devices. At present, there remains no one single strategy for the management of the particular stages of DR and DME as there are many options that have not been rigorously tested through large, randomized, controlled clinical trials.ConclusionPharmacotherapy, both ocular and systemic, will be the primary mode of intervention in the management of DR and DME in many cases when cost and treatment burden are less constrained. Conventional laser therapy has become a secondary intervention in these instances, but remains a first-line option when cost and treatment burden are more constrained. Results with subthreshold laser appear promising but will require more rigorous study to establish its role as adjunctive therapy. Evidence to support an optimal integration of the various treatment options is lacking. Central to the widespread adoption of any therapeutic regimen for DR and DME is substantiation of safety, efficacy, and cost-effectiveness by a body of sound clinical trials.

The detection of diabetic retinopathy (DR) is challenging, as the current diagnostic methods rely heavily on the expertise of specialists and require the mass screening of diabetic patients. The prevalence of avoidable vision impairment due to DR necessitates the exploration of alternative diagnostic techniques. Specifically, it is necessary to develop reliable automatic methods to enable the early diagnosis and detection of DR from optical images. To address the lack of such methods, this research focused on employing various pre-trained deep neural networks (DNNs) and statistical metrics to provide an automatic framework for detecting DR in optical images. The receiver operating characteristic (ROC) was employed to examine the performance of each network. Ethically obtained real datasets were utilized to validate and enhance the robustness of the proposed detection framework. The experimental results showed that, in terms of the overall performance in DR detection, ResNet-50 was the best, followed by GoogleNet, with 99.44% sensitivity, while they were similar in terms of accuracy (93.56%). ResNet-50 outperformed GoogleNet in terms of the specificity (89.74%) and precision (90.07%) of DR detection. The ROC curves of both ResNet-50 and GoogleNet yielded optimal results, followed by SqueezeNet. MobileNet-v2 showed the weakest performance in terms of the ROC, while all networks showed negligible errors in diagnosis and detection. These results show that the automatic detection and diagnosis framework for DR is a promising tool enabling doctors to diagnose DR early and save time. As future directions, it is necessary to develop a grading algorithm and to explore other strategies to further improve the automatic detection and diagnosis of DR and integrate it into digital slit lamp machines.

Diabetic Retinopathy (DR) is a chronic disease that may cause vision loss in diabetic patients. A regular eye screening is essential to grade the stages of DR such as Microaneurysms (MAs), exudates and drusen in retinal images acquired using fundus camera. Microaneurysm is an early stage of DR, characterized by small red spots on the retina due to blood and fluid leakage from the weak capillary wall. Hence early detection is vital in preventing the diabetic retinopathy and this article explores an automatic screening system that focus on the early detection of DR which is referred as Microaneurysm. The proposed automatic decision system follow the stages of acquisition of color fundus images, pre-processing the input fundus image, manual selection of Microaneurysm area by Region Of Interest (ROI) and classification of diabetic retinopathy is more helpful for early detection and analysis of diabetic retinopathy. The detection process comprises of image pre-processing, feature extraction and classification methods. The proposed method has been applied to color fundus image in feature extraction, classification and provided with improved outcomes for detecting Microaneurysm.

Diabetic Retinopathy (DR) is a major cause of blindness which is caused by damage to the small blood vessels of the retina. Early detection of Diabetic Retinopathy is important to prevent vision loss. Main two stages of diabetic retinopathy are non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). Microaneurysms, hemorrhages, cotton-wool spots and exudates are symptoms of non proliferative diabetic retinopathy which is the early stage of diabetic retinopathy. Of these symptoms exudates detection is more important for the early detection of diabetic retinopathy. In this paper, we are proposing a method for the automated detection of exudates by using retinal images. We show that different morphological operations could be incorporated in order to detect exudates quite accurately. In the process of exudates detection optic disk detection is an important part. Otherwise, optic disk could be wrongly identified as exudates. We have used different morphological operations for the exudates detection from the retinal images and the developed algorithm has a sensitivity and specificity 94.59% and 88.46% respectively. This is considerably a higher success rate with the existing systems.

Assessing the Severity of Diabetic Retinopathy: Early Treatment Diabetic Retinopathy Study Report Number 10

Aims/HypothesisThere is evidence that diabetes is accompanied by a break-down of functional hyperemia, an intrinsic mechanism of neural tissues to adapt blood flow to changing metabolic demands. However, to what extent functional hyperemia is altered in different stages of diabetic retinopathy (DR) in patients with type II diabetes is largely unknown. The current study set out to investigate flicker-induced retinal blood flow changes in patients with type II diabetes at different stages of DR.Materials and methodsA total of 76 subjects were included in the present parallel-group study, of which 56 had diabetes with either no DR or different stages of non-proliferative DR (n = 29 no DR, 12 mild DR, 15 moderate to severe DR). In addition, 20 healthy subjects were included as controls. Retinal blood flow was assessed before and during visual stimulation using a combined measurement of retinal vessel calibers and blood velocity by the means of Doppler optical coherence tomography (OCT). To measure systemic autonomic nervous system function, heart rate variability (HRV) was assessed using a short-term orthostatic challenge test.ResultsIn healthy controls, retinal blood flow increased by 40.4 ± 27.2% during flicker stimulation. Flicker responses in patients with DR were significantly decreased depending on the stage of the disease (no DR 37.7 ± 26.0%, mild DR 26.2 ± 28.2%, moderate to severe DR 22.3 ± 13.9%; p = 0.035, ANOVA). When assessing systemic autonomous neural function using HRV, normalized low frequency (LF) spectral power showed a significantly different response to the orthostatic maneuver in diabetic patients compared to healthy controls (p < 0.001).Conclusion/InterpretationOur study indicates that flicker induced hyperemia is reduced in patients with DR compared to healthy subjects. Further, this impairment is more pronounced with increasing severity of DR. Further studies are needed to elucidate mechanisms behind the reduced hyperemic response in patients with type II diabetes.Clinical trial registration[https://clinicaltrials.gov/], identifier [NCT03 552562].

Subfoveal Choroidal Thickness in Diabetes and Diabetic Retinopathy

Objective To investigate the distribution patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT), and explore its correlation with diabetic retinopathy (DR) stages and systemic factors. Methods A total of 135 patients (242 eyes) with type 2 diabetes were included in this retrospective study. There were 75 males (138 eyes) and 60 females (104 eyes), the ages were from 29 to 83 years, with an average age of (58.8±11.1) years. The general information such as height, weight, smoking history and blood glucose [such as glycosylated hemoglobin (HbA1c)], blood pressure, blood lipid, 24 hours urine protein and other examinations were collected. The diagnosis of DR and DME were made, and the staging of DR and typing of DME were performed based on fundus color imaging and OCT. DR were divided into mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR and proliferative DR (PDR). DME were categorized into 4 types including sponge-like retinal swelling (SME), cystoid macular edema (CME), serous retinal detachment (SRD) and posterior hyaloid traction (PHT). The correlation between DME types and DR staging were analyzed by χ2 test and Fisher exact test. Multivariate logistic regression analysis was used to analyze the correlation between DME types and systemic factors. Results In 242 DR eyes the proportions of mild, moderate, severe NPDR and PDR were 30.99%, 32.64%, 23.14% and 13.23%, respectively. There were 199 eyes (82.23%) with DME. There were statistically significant differences in the proportion of DME in different stages of DR (χ2=21.077, P<0.01). In the 199 eyes with DME, There were 165 eyes (68.18%) of SME, 22 eyes (9.09%) of CME, 7 eyes (2.89%) of SRD and 5 eyes (2.07%) of PHT. The distribution of DME patterns in different stages of DR was statistically significant (χ2=156.273, P<0.01). Logistic regression analysis showed that the duration of diabetes, HbA1c and macroalbuminuria were independent risk factors for DME [odds ratio (OR)= 1.090, 1.510, 4.123; P<0.05], and were also independent for SME (OR=1.092, 1.445, 3.942; P<0.05); HbA1c was an independent risk factor for SRD (OR=2.337, P<0.05). Conclusions There are differences in the distribution of different DME types in each stage of DR. The duration of diabetes, HbA1c and macroalbuminuria were independent risk factors for DME and SME, and macroalbuminuria and HbA1c for CME and SRD. Key words: Macular edema/diagnosis; Risk factors; Diabetic retinopathy/diagnosis; Tomography, optical coherence

Background: Diabetic Retinopathy (DR), a significant cause of vision loss globally, is characterized by retinal damage caused by diabetes. Early detection is vital to prevent irreversible blindness, yet challenges remain in accurately identifying DR stages and enhancing blood vessel visibility in fundus images. This paper aims to develop an early detection methodology for DR, addressing the need for early diagnosis and the difficulties in distinguishing DR severity through fundus imaging. The challenges in the early detection of Diabetic Retinopathy (DR) and enhancing blood vessel visibility in fundus images are multifaceted, including issues such as data imbalance, image noise, and complex patterns. By addressing these challenges through advanced ML techniques and image processing methodologies, the proposed methodology in the paper aims to overcome the limitations in early detection and severity assessment of DR, contributing to improved patient outcomes and vision preservation. Methods: This study utilizes Machine Learning (ML) to analyze complex patterns in fundus color images of DR, employing spatial domain filtering to reduce image noise and address data imbalances across DR severity levels through data augmentation. A Convolutional Neural Network (CNN), enhanced with a Gabor filter, is applied for stage-specific DR detection and to pinpoint infected areas. The dataset includes 1000 color fundus images, with a 70:30 split for training and testing, respectively. The adoption of a Gabor filtering technique aims to refine the model’s performance further. Results: The incorporation of a CNN with a Gabor filter has shown outstanding efficacy in detecting DR from fundus color images, achieving a training accuracy of 97.5%, validation accuracy of 96.5%, Cohen kappa score of 89.76%, and testing accuracy of 95.87%. This method effectively illustrates the disease-affected areas in the fundus images provided. Conclusion: Through a comparative analysis of image processing techniques, this research highlights the advantages of using advanced DR analysis for image preprocessing. The proposed CNN-Gabor filter approach demonstrates significant success in identifying diabetic retinopathy in fundus color photographs and accurately delineating the affected regions, contributing valuable insights to the field of medical image processing.

Early detection and treatment are important for preventing vision loss from diabetic retinopathy. Historically, the gold standard for grading diabetic retinopathy has been based on 7-field 30-degree color fundus photographs that capture roughly the central third of the retina. Our aim was to review recent literature on the role of ultra-widefield (allowing capture of up to 82% of the retina in one frame) fundus imaging in screening, prognostication, and treatment of diabetic retinopathy. Ultra-widefield fundus imaging can capture peripheral retinal lesions outside the traditional 7-field photographs that may correlate with increased risk of diabetic retinopathy progression. The speed and ability to image through undilated pupils make ultra-widefield imaging attractive for tele-ophthalmology screening. Ultra-widefield fluorescein angiography may help guide targeted laser treatment in eyes with proliferative diabetic retinopathy. Ultra-widefield imaging has potential to help shape new diabetic retinopathy screening, staging, and treatment protocols.

Diabetic retinopathy (DR) is a vascular complication of diabetes that can lead to partial or complete loss of vision. Early detection and treatment of DR can prevent blindness. Regular clinical examination is recommended for DR diagnosis; however, it is not always possible or feasible due to limited resources, expertise, time, and infrastructure. Several clinical and molecular biomarkers are proposed for the prediction of DR including microRNAs. MicroRNAs are a class of small non-coding RNAs that are found in biofluids and can be measured using reliable and sensitive methods. The most commonly used biofluid for microRNA profiling is plasma or serum; however, tear fluid (tears) is also demonstrated to contain microRNAs. MicroRNAs isolated from tears present a non-invasive source for DR detection. Different methods of microRNA profiling are available including digital PCR-based methods that can detect up to a single copy of microRNA in the biofluids. Here, we describe microRNA isolation from tears using manual method as well as using a high-throughput automated platform followed by microRNA profiling using digital PCR system.