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Purpose A model was calculated during the first Austrian coronavirus disease-2019 (COVID-19) pandemic lockdown to estimate the effect of a short-term treatment interruption due to healthcare restrictions on visual acuity (VA) in neovascular age-related macular degeneration (nAMD). The model was compared to the real-life outcomes before treatment re-started. Methods Retrospective data-collection of 142 eyes in 142 patients receiving repeated intravitreal injections with anti-VEGF at a retina unit in Vienna in a personalized pro-re-nata regimen prior to the COVID-19 associated lockdown, when treatment was deferred between March 16 and May 4, 2020. During the lockdown, the preliminary data was integrated into pre-existing formulae based on the natural course of the disease in untreated eyes in the long term. Patients were re-scheduled and treated after gradually opening operating rooms. The calculation model was compared to the effective VA change. Results The model calculated an overall VA loss of 3.5 ± 0.8 letters early treatment diabetes retinopathy study (ETDRS) ( p Conclusion The herein presented calculation model might not be suitable to estimate the effective VA loss correctly over time, although untreated eyes and eyes under therapy show similarities after short-term treatment interruption. However, this study demonstrated the potentially negative impact of the COVID-19 pandemic lockdown on patients compromised by nAMD.

Despite progress in the treatment of diabetic macular edema and diabetic retinopathy, the rate of lower fundus examination due to limitations of medical resources delays the diagnosis and treatment of diabetic retinopathy. Therefore, implementation of automated diabetic retinopathy screening program and the identification of more specific and sensitive biomarkers are important for facilitating the earlier detection of diabetic macular edema and diabetic retinopathy to decrease the prevalence of poor vision and blindness.

Penyakit diabetic retinopathy atau DR adalah salah satu komplikasi penyakit diabetes yang bisa menyebabkan kematian bagi penderitanya. Komplikasi tersebut berupa kerusakan pada bagian retina mata. Tingginya kadar glukosa dalam darah adalah penyebab pembuluh darah kapiler kecil menjadi pecah dan dapat menyebabkan kebutaan. Retinopati diabetes diawali dengan melemah atau hancurnya kapiler kecil di retina, darah bocor yang kemudian menyebabkan penebalan jaringan, pembengkakan, dan pendarahan yang luas. Penelitian ini bertujuan untuk menganalisis diagnosis penyakit diabetes retinopathy. Algoritme Classification And Regression Trees (CART) merupakan salah satu algoritme klasifikasi dengan menggunakan dataset diambil dari UCI Repository Learning diperoleh dari Universitas Debrecen, Hongaria, yang terdiri dari data pasien terindikasi penyakit diabetes retinopathy dan normal penyakit diabetes retinopathy. Metode yang digunakan dalam penelitian ini yaitu identifikasi masalah, pengumpulan data, tahap pre-processing, metode klasifikasi, validasi dan evaluasi serta penarikan kesimpulan. Adapun metode validasi dan evaluasi yang digunakan yaitu 10-cross validation dan confusion matrix.Dari hasil perhitungan yang telah dilakukan, maka didapatkan hasil akurasi pada algoritme CART sebesar 63.4231%, dengan nilai precision 0.64%, nilai Recall 0.634%, dan nilai F-Measure 0,634%.

In recent years, the diabetes mellitus in Indonesia has become a health problem in the community because its population has increased 2-3 times faster than other countries. Diabetes prevalence in Indonesia ranks 4th highest in the world after China, India and the United States. People can prevent complications and premature death if they detect early symptoms of diabetes. However, people do not know that they are at risk of diabetes, not had knowledge about the symptoms of diabetes, complexity of the process diagnosis and the high cost of examinations. Therefore, we need an application that can provide the results of the type of diabetes and its management solutions as practiced by experts. The aim of this research is to develop an expert system for detection types of diabetes such as: type one diabetes, type two diabetes, neuropathy diabetes, diabetes retinopathy, and diabetes nephropathy. The object of this research is diabetes carried out in March to April 2019 in the Klinik Pratama Desa Putera. This study uses primary data from patients who had a history of diabetes at Klinik Pratama Desa Putra and secondary data in the form of literature, research journals, and data documents needed to compile this study. In addition, we generated a knowledge base using forward chaining. The test results show that the expert system meets the functional requirements and the system performance reaches an accuracy of 100%. This expert system helps people in Indonesia to detect diabetes early so that it can prevent complications.

Diabetes retinopathy (DR) is the leading cause of blindness and is considered as the most ordinary microvascular complication of diabetes mellitus (DM). The current available therapies can only be effective in the progressive stages of DR and there are various clinical studies which depicts the inconclusive outcomes of these available therapies after assessing the long-term efficacy and safety of such treatments. Moreover, there is an indispensable need of a more reliable as well as potent treatment which can be considered as more efficacious treatment for the management of DR. The treatment of DR can be possible at the early stages with polyphenols which are plant derived chemical agents and can be act as an alternative to other treatments and can optimistically prevent the further evolution of disease. The present review emphasizes the role of polyphenols on cellular and molecular mechanisms altered by DR in preclinical and clinical studies.

Objectives: Management of various macular diseases depends on macular thickness which is measured quantitatively by optical coherence tomography. Studies have reported variations in the macular thickness by race and gender. The aim of this study was to determine the normal macular thickness measurements in healthy eyes of Libyans. Subjects and Methods: This study was conducted at the Ophthalmology Outpatient Department at Alkeish polyclinic in the period between January and December 2018. This study included 243 healthy eyes of 131 Libyan adults of both genders who underwent a complete ophthalmic examination including spectral domain optical coherence tomography to measure the macular thickness at the nine areas corresponding to Early Treatment Diabetes Retinopathy Study map (ETDRS). Results: The mean age of the study population was 48.3 ± 16.6 years (ranged between 21 and 79 years), the thickness in the foveola/center point of macula (CPT) was 192 ± 22.4 μm, the central foveal thickness was 230.3 ± 18.3 μm, and the average thickness was 270.1 ± 9.4 μm. Males were having more thickness than females. Conclusions: Foveola's thickness (CPT) in Libyan adults measured by spectral domain optical coherence tomography is thinner than that of previously published studies. Moreover, the central foveal thickness is less than that of many other studies and males have more thickness than females in all the areas of ETDRS map, which indicates that gender must be taken into consideration while interpreting macular retinal thickness data.

BACKGROUND India is emerging as worlds’ diabetic capital. The cases of diabetic retinopathy are also increasing. We wanted to determine whether contrast sensitivity can be used as early indicator in diabetic retinopathy and also to measure and compare the contrast sensitivity in adults with type 2 diabetes mellitus with retinopathy and post-laser photocoagulation and correlate the contrast sensitivity in patients without diabetic retinopathy. METHODS This cross-sectional study included 50 diabetic patients with and without retinopathy and also 30 patients who had undergone laser photocoagulation who were above 30 years. Detailed history was obtained; ocular examination, and measurement of contrast sensitivity with Pelli Robson E and alphabet chart were done. Statistical analysis was done using Spearman’s coefficient of correlation, analysed using Epi Data / SPSS. RESULTS Contrast sensitivity was found to be reduced in post laser photocoagulation group and retinopathy group when compared to no retinopathy group. CS value between ‘No Retinopathy’ and ‘Post Laser’ groups, the p value by unpaired t test was found be statistically significant (p

Diabetes retinopathy (DR) is one of the leading cause of blindness among people suffering from diabetes. It is a lesion based disease which starts off as small red spots on the retina. These small red lesions are known as microaneurysms (MA). These microaneurysms gradually increase in size as the DR progresses, which eventually leads to blindness. Thus, DR can be prevented at a very early stage by eliminating the retinal microaneurysms. However, elimination of MA is a two step process. The first step requires detecting the presence of MA on the retina. The second step involves pinpointing the location of MA on the retina. Even though, these two steps are interdependent, there is no model available that can perform both steps simultaneously. Most of the models perform the first step successfully, while the second step is performed by opthamologists manually. Hence we have proposed an object detection model that integrates the two steps by detecting (first step) and pinpointing (second step) the MA on the retina simultaneously. This would help the opthamologists in directly finding the exact location of MA on the retina, thereby simplifying the process and eliminating any manual intervention.

Diabetic Retinopathy (DR) is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. Aim: The focus of this paper is to evaluate the performance of Decision Tree (DT), Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) Classifiers in Diabetes Retinopathy Detection. Results: Corresponding results showed SVM has the best classification strength by achieving Recognition Accuracy (RA) of 98.50%, while PNN and DT achieved RA of 97.60% and 89.20% respectively. In terms of False Acceptance Rate (FAR) and False Rejection Rate (FRR), SVM has the least values of 7.21, 8.10 while DT and PNN showed 11.10, 9.30 and 13.21, 10.10 respectively. However, in this paper a Mobile based Diabetes Retinopathy Detection System was developed to make the system available for the masses for early detection of the disease.

(1) Background: Diabetic retinopathy, one of the most serious complications of diabetes, is the primary cause of blindness in developed countries. Therefore, the prediction of diabetic retinopathy has a positive impact on its early detection and treatment. The prediction of diabetic retinopathy based on high-dimensional and small-sample-structured datasets (such as biochemical data and physical data) was the problem to be solved in this study. (2) Methods: This study proposed the XGB-Stacking model with the foundation of XGBoost and stacking. First, a wrapped feature selection algorithm, XGBIBS (Improved Backward Search Based on XGBoost), was used to reduce data feature redundancy and improve the effect of a single ensemble learning classifier. Second, in view of the slight limitation of a single classifier, a stacking model fusion method, Sel-Stacking (Select-Stacking), which keeps Label-Proba as the input matrix of meta-classifier and determines the optimal combination of learners by a global search, was used in the XGB-Stacking model. (3) Results: XGBIBS greatly improved the prediction accuracy and the feature reduction rate of a single classifier. Compared to a single classifier, the accuracy of the Sel-Stacking model was improved to varying degrees. Experiments proved that the prediction model of XGB-Stacking based on the XGBIBS algorithm and the Sel-Stacking method made effective predictions on diabetes retinopathy. (4) Conclusion: The XGB-Stacking prediction model of diabetic retinopathy based on biochemical and physical data had outstanding performance. This is highly significant to improve the screening efficiency of diabetes retinopathy and reduce the cost of diagnosis.

Diabetes, retinopathy, and cardiovascular diseases are alarmingly increasing day by day due to malnutrition and unhealthy lifestyle. Dietary life style may be the initial step for prevention of diabetes and cardiovascular diseases. Some other factors are also associated with life style like physical exercises, daily intake of vegetable, fruit, cereal, omega-3 fats, and tobacco and alcohol consumption. It is a common quote that eats healthy to stay healthy. Same is suggested by scientific data that carotenoids are important in the prevention of these diseases because of antioxidant and antiinflammatory action. Carotenoids have been proved by epidemiological studies to be protective against cardiovascular disease. All these reports need in vitro and intervention studies in well-controlled conditions so that these results will be more valid. Mixed results have been reported from interventional trials with some positive and many insignificant outcomes. These discoveries also recommended some harm in certain high-risk populations. The objective of this chapter is to summarize the writings accessible about the consumption of carotenoids in the management of cardiovascular disease, retinopathy, and diabetes.

Diabetes retinopathy (DR) is a disease that afflicts diabetic patients and is caused by damage to the blood vessels in the retina of the eye. This disease is a leading cause of blindness and is a common complication of diabetes. Regular screening and early detection are crucial to treatment. Automated classification with machine learning techniques has shown promise in this regard. Previous work has not addressed the imbalances in the datasets used. In this paper, we report on the effectiveness of data augmentation techniques for balancing the datasets and thus improving the accuracy of automated diagnosis of DR from fundus images. We achieve state of the art results – an F1 score of 0.8 and an accuracy of 86.96% by using data augmentation to balance the dataset.

Diabetic retinopathy (DR) is the leading cause of blindness in patients with diabetes. However, biomarkers for early detection of DR are still lacking. MicroRNAs (miRNAs) regulate multiple biological functions and are often deregulated in DR. We aimed to investigate whether circulating miRNAs can be used as biomarkers of early-stage DR. We used RNA-seq and qRT-PCR to identify differential serum miRNAs in patients with type 2 diabetes mellitus with DR (T2DM-DR), T2DM without DR (T2DM-no-DR), and healthy controls. We validated differential circulating miRNAs in two phases using qRT-PCR assays. RNA-seq analysis identified 7 differential circulating miRNAs between T2DM-DR and T2DM-no-DR and 47 differential miRNAs between T2DM-DR and healthy subjects. Two-stage analysis verified that a profile of five serum miRNAs (hsa-let-7a-5p, hsa-miR-novel-chr5_15976, hsa-miR-28-3p, has-miR-151a-5p, has-miR-148a-3p) was significantly associated with T2DM-DR. Receiver-operator-characteristic analyses showed that a panel of three miRNAs (hsa-let-7a-5p, hsa-miR-novel-chr5_15976, and hsa-miR-28-3p) presented 0.92 sensitivity and 0.94 specificity for distinguishing T2DM-DR from T2DM-no-DR, and 0.93 sensitivity and 0.86 specificity for differentiating early-stage T2DM-DR (NPDR) from late-stage DR (PDR). Lentivirus-mediated overexpression of hsa-let-7a-5p in human retinal microvascular endothelial cells (HRMECs) significantly promoted proliferation rates of HRMECs. In conclusion, the three-miRNA signature from serum may serve as a noninvasive diagnostic biomarker for DR. Furthermore, we showed that DR-associated miRNAs may be involved in the pathogenesis of DR, at least in part, through modifying proliferation of HRMECs.

Diabetic Retinopathy is a major cause of vision loss and blindness affecting millions of people across the globe. Although there are established screening methods - fluorescein angiography and optical coherence tomography for detection of the disease but in majority of the cases, the patients remain ignorant and fail to undertake such tests at an appropriate time. The early detection of the disease plays an extremely important role in preventing vision loss which is the consequence of diabetes mellitus remaining untreated among patients for a prolonged time period. Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect of data pre-processing and dimensionality reduction, leading to biased results. The dataset used in the present study is a diabetes retinopathy dataset collected from the UCI machine learning repository. At its inceptions, the raw dataset is normalized using the Standardscalar technique and then Principal Component Analysis (PCA) is used to extract the most significant features in the dataset. Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification. The results generated from the model is evaluated against the prevalent machine learning models and the results justify the superiority of the proposed model in terms of Accuracy, Precision, Recall, Sensitivity and Specificity.

Retinopathy screening is a non-invasive method to collect retinal images and neovascularization detection from retinal images plays a significant role on the identification and classification of diabetes retinopathy. In this paper, an automatic parallel detection framework for neovascularization with color retinal images using ensemble of extreme learning machine is proposed. The framework employs two Map-Reduce Jobs to extract features and trains Extreme Learning Machine models. Ensemble methods such as bagging, subspace partitioning and cross validating are used to increase the accuracy. The framework is evaluated with retinal images from MESSIDOR database. Experimental results show the framework can improve the detection accuracy, as well as speedup the processing time to 22 times on average.

Background: Diabetic retinopathy (DR) is a common diabetes complication and was considered as the major cause of blindness among young adults. MiRNAs are a group of small non-coding RNAs regulating the expression of target genes, and have been reported to be associated with the development of diabetic retinopathy in a variety of molecular mechanisms. In this study, we aimed to identify miRNAs that are differentially expressed in the serum of DR patients. Methods: We recruited 21 type 2 diabetes mellitus (T2DM) inpatients of Chinese Han ancestry, consisted of 10 non-proliferative DR patients (DR group) and 11 non-DR T2DM patients (NDR group). MiRNA was extracted from fasting peripheral serum and quantified by RNA-seq. The expression levels of miRNA was evaluated and compared between the two groups, with the adjustment of age. The validated target genes of miRNAs were subjected to a pathway analysis. We also constructed a weighted polygenic risk score using the differentially expressed miRNA and evaluated its predictive power. Results: Five miRNAs were differentially expressed between DR and NDR groups (p value ≤ 0.01, LFC ≥ 2 or LFC ≤ -2). These included miR-4448, miR-338-3p, miR-190a-5p, miR-485-5p, miR-9-5p. In total, these miRNAs was validated to regulate 55 target genes. Four target genes were found to overlap with the NAD metabolism, sirtuin and aging pathway, which was thought to control the vascular growth and morphogenesis. The predictive power of our polygenic risk score was apparently high (AUC = 0.909). However, it needs to be interpreted with caution. Conclusion: In this study, we discovered novel DR-specific miRNAs in human serum samples. These circulating miRNAs may represent the pathological changes in the retina in response to diabetes and may serve as non-invasive biomarkers for early DR risk prediction.

Este artículo se ha publicado de forma definitiva en: https://www.mdpi.com/2076-3921/8/3/53 Este artículo pertenece al número especial "Antioxidants and Retinal Disease". Oxidative stress has been documented to be a key factor in the cause and progression of different retinal diseases. Oxidative cellular unbalance triggers a sequence of reactions which prompt cell degeneration and retinal dysfunction, both hallmarks of several retinal pathologies. There is no effective treatment, yet, for many retinal diseases. Antioxidant treatment have been pointed out to be an encouraging palliative treatment; the beneficial effects documented involve slowing the progression of the disease, a reduction of cell degeneration, and improvement of retinal functions. There is a vast information corpus on antioxidant candidates. In this review, we expose three of the main antioxidant treatments, selected for their promising results that has been reported to date. Recently, the sulforaphane, an isothiocyanate molecule, has been unveiled as a neuroprotective candidate, by its antioxidant properties. Progesterone, a neurosteroid has been proposed to be a solid and effective neuroprotective agent. Finally, the lipoic acid, an organosulfur compound, is a well-recognized antioxidant. All of them, have been tested and studied on different retinal disease models. In this review, we summarized the published results of these works, to offer a general view of the current antioxidant treatment advances, including the main effects and mechanisms described.

Purpose: To analyse the effects of intravitreal dexamethasone implant (DEX) in patients with diabetic macular oedema (DME) unresponsive to ranibizumab treatment, in relation to the inflammatory optical coherence tomography (OCT) retinal features, subfoveal neuroretinal detachment (SND) and hyperreflective retinal spots (HRS). Methods: Patients with DME poorly responsive to three injections of ranibizumab were treated with DEX. Best-corrected visual acuity (BCVA) and central macula thickness (CMT, measured by Spectralis SD-OCT) were assessed at baseline and at 1, 3, and 6months. Results: Overall, 44 eyes were included in the study. In the whole group, mean BCVA (baseline 51.5±8.3 letters) increased significantly at 1month (to 56.9±8.8 letters; Tukey HSD p=0.017) and was 55.5±8.8 letters at 3months (Tukey HSD p=0.128). Central macula thickness (CMT) reduced significantly at 1 and 3months (417±149μm and 469±128μm, respectively, both Tukey HSD p&nbsp

Diabetes Retinopathy is a disease which results from a prolonged case of diabetes mellitus and it is the most common cause of loss of vision in man. Data mining algorithms are used in medical and computer fields to find effective ways of forecasting a particular disease. This research was aimed at determining the effect of using feature selection in predicting Diabetes Retinopathy. The dataset used for this study was gotten from diabetes retinopathy Debrecen dataset from the University of California in a form suitable for mining. Feature selection was executed on diabetes retinopathy data then the Implementation of k-Nearest Neighbour, C4.5 decision tree, Multi-layer Perceptron (MLP) and Support Vector Machines was conducted on diabetes retinopathy data with and without feature selection. There was access to the algorithms in terms of accuracy and sensitivity. It is observed from the results that, making use of feature selection on algorithms increases the accuracy as well as the sensitivity of the algorithms considered and it is mostly reflected in the support vector machine algorithm. Making use of feature selection for classification also increases the time taken for the prediction of diabetes retinopathy.

Objective Data regarding diabetes retinopathy and associated factors are currently lacking in Ethiopia. The study aims to determine the incidence and determinants of time to diabetes retinopathy among diabetes mellitus patients at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia. Results The incidence of diabetes retinopathy is a rapidly growing burden of disease in Ethiopia. The incidence rate of diabetes retinopathy was 2.65 (95% CI 2. 54, 4.05) per 1000 person-years observation. Moreover, 70 (18.57%, 95% CI 14.63, 22.5) DM patients developed diabetes retinopathy. The median time was 74.07 months (with IQR 53.60, 89.88). Male sex (AHR = 1.94, 95% CI = 1.10, 3.39), type 2 DM (AHR = 4.01, 95% CI = 1.34, 12.00), creatinine (AHR = 2.59, 95% CI = 1.91, 3.52), borderline triglyceride (AHR = 2.87, 95% CI 1.33, 6.21) and high triglyceride levels (AHR = 2.59, 95% CI = 1.31, 4.97) were positively correlated factors to diabetes retinopathy occurrence. Multisectoral, population-based approaches are needed to reduce type 2 DM complications.

Young persons who develop DR may represent a high-risk group for early development of other complications. In young persons with youth-onset T1D, we evaluated associations between DR status and markers of other T1D complication risk. Youth (N=127), mean age 21.7 y (range 10.7-31.9), mean T1D onset 6.3 y (range 0.5-17.6) underwent DR assessment (ETDRS-protocol 7 field fundus photos); renal, neuropathic, and CVD risk screenings; and risk screening by blood/urine studies. DR was classified as none (No DR, 37%), mild nonproliferative (Mild DR, 46%), and more than mild NPDR (Advanced DR, 17%). More severe DR was associated with greater age, T1D duration, A1c, and weight status (Table). DR severity was associated with neuropathy, vascular stiffness by pulse wave (aortic AIx), and inflammatory, oxidative stress, and endothelial activation markers (CRP, isoprostane, e-selectin). Paradoxically, greater DR was associated with less PAI-1. After adjustment for age, T1D duration, weight, and A1c by ANCOVA, DR status was associated with differences in cooling threshold (7.6, 8.6, 12.3, p=.0003) and e-selectin (39.8, 34.0, 46.0, p=.02). Young persons with DR have high risk for neuropathic, renal, and CVD complications. While PAI-1 is lower, markers of inflammation and oxidative stress are higher in this group. Optimal glycemic control must be prioritized to prevent or delay DR progression and other complications in young persons with DR. Unadjusted Associations Between Clinical Factors and DR Status, *indicates ln transformed p valueClinical FactorNo DR (n=47) Mean or Median or %Mild DR (n=59) Mean or Median or %Advanced DR (n=21) Mean or Median or %P value ANOVA or Chi-squareAge (yrs)/T1D Duration (yrs)/A1c (%)18.9/13.2/8.022.6/15.8/8.625.8/19.6/9.1 Disclosure M. Katz: None. G.L. King: Research Support; Self; Sanofi-Aventis. J. Sun: Other Relationship; Self; Novartis Pharmaceuticals Corporation. Research Support; Self; Genentech, Inc., Optovue, Incorporated, Boston Micromachines Corporation, Adaptive Sensory Technology, Kalvista Pharmaceuticals, Inc.. Other Relationship; Self; Novo Nordisk Inc. L.M. Laffel: Consultant; Self; Eli Lilly and Company, Novo Nordisk Inc., Sanofi US, MannKind Corporation, Roche Diagnostics Corporation, Dexcom, Inc., Insulet Corporation, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Johnson & Johnson Diabetes Institute, LLC..

INTRODUCTION: Diabetic retinopathy (DR) is a leading cause of blindness worldwide. In Saudi Arabia and other Arab countries, several studies estimated the prevalence of DR ranging from 30 - 40%.AIM: To assess the DR knowledge and its association with diabetes control among Type 2 diabetic patients.METHODS: A cross-sectional study of patients with Type II diabetes (T2D) who had a routine visit to the endocrine clinic to assess the DR knowledge and its relation to the glycemic control. We used a questionnaire that was used in previously published studies, and the reliability was assessed using the alpha Cronbach coefficient. Patients who answered correctly > 60% were considered to have good knowledge about DR.RESULTS: Total of 253 patients participated, 43.4% has diabetes > 10 years and 30.7% have it for 5 - 10 years, 36.4% did college degree or higher, 40.8% considered having low income. 37.7% of participants were not screened for the DR in the past year. 28.4% of participants think that seeing optometrist is enough for DR diagnosis. Diabetics with good knowledge who have T2D > 10 years were 46.3% compared to 38.6% (p = 0.04). Diabetics with good knowledge have mean A1c of 8.55 vs. 8.59 (p = 0.32), mean BMI 30.4 vs. 30.2 (p = 0.46), mean diastolic pressure was 77.12% vs. 79.48% (p = 0.03).CONCLUSION: Almost two-thirds of screened T2D were considered to have good knowledge about DR. The good knowledge group tends to have a longer duration of T2D, more likely to have a college degree, and tend to have non -significantly better A1c control.

Diabetic retinopathy (DR) is caused by damage the retina because fluid leaks from blood vessels into the retina. Damage the posterior part of the eye of the diabetic patient. This disease that occurs when does not secrete enough insulin or the body is unable to process it properly. The main two types of diabetic retinopathy the first are non-proliferate diabetes retinopathy (NPDR) and second are proliferate diabetes retinopathy (PDR). The increasing number of DR cases world-wide demands to the development of an automated detection system. We have proposed a computer based method for the detection of diabetic retinopathy using the fundus images. Using Image pre-processing, morphological processing techniques involves processing of fundus images for detect features, such as blood vessel area, exudates, microaneurysms, hemorrhages, and texture. Proposed techniques used for the extraction of these features from digital fundus images. The proposed techniques have been tested on the images of DIARETDB0 database. In which have total 130 images they all images are tested and it’s classified into microaneurysms, hemorrhages, and texture using Support Vector Machine (SVM) for an automatic classification. The detection results obtained by comparing it with expert ophthalmologists. We demonstrated a classification sensitivity of 96.43%, specificity of 95.9 % and accuracy of 99.27 %.

The identification of MAs is an important phase in the research and grading of suffering from diabetes retinopathy. We present clustering strategy to identify the microaneurysms from the optic disk and cup in the retinal fundus pictures. Fuzzy C-Means (FCM) Clustering is used for clustering the information in which the information factors are grouped with different account level. The first and major phase is preprocessing function, in which the optic cup and disk of the feedback picture is being turned. Originally the optic hard disk is turned in some position and the range between the information factors is calculated and a group is established in accordance with the centroid. For retrieving micro aneurysms in all retinal images in our previous work we used SVM Classification filter in Fuzzy C-Means Clustering. In this paper we propose an effective filtering technique for micro aneurysms detection in retinal image preprocessing. Instead of SVM Filtering technique we used NLM in terms of Anisotropic Filter to process retinal images. Tested on the various simulated retina data repositories combining rotation and scaling, the developed method presents good results and shows robustness to rotations and scale changes.

Daily glucose variability, such as fasting plasma glucose fluctuation or postprandial hyperglycemia, has been proposed as contributors to diabetes-related macrovascular complications. However, its impacts on microvascular complications, such as diabetes retinopathy remain controversial. We reviewed the current evidence of the relationship between glycemic variability and diabetes retinopathy in patients with type 1 or type 2 diabetes. In general, the short-term glycemic fluctuation, either expressed as standard deviation of fasting glucose or mean glucose levels, may contribute to the development or progression of diabetic retinopathy in patients with type 2 diabetes, whereas long-term glycemic fluctuation, reflected by variation of levels of HbA1c, appeared to show a stronger association with diabetes retinopathy both in patients with type 1 and type 2 diabetes. These findings emphasize the need to reduce glycemic variability by various measures in order to reduce development and progression of diabetes retinopathy both in type 1 and type 2 diabetes patients.

Many health-related problems arise with aging. One of the diseases that is prevalent among the elderly is the loss of sight. Various eye diseases, namely age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are the prime causes of vision loss as we grow old. Nevertheless, early detection of such eye diseases can impede the progression of this problem. Therefore, the elderly are encouraged to attend regular eye checkups for early detection of eye diseases. However, it is time-consuming and laborious to conduct a mass eye screening session frequently. Hence, we proposed a novel approach to develop an automated retinal health screening system in this work. This paper discusses a retinal screening system to automatically differentiate normal image from abnormal (AMD, DR, and glaucoma) fundus images. The fundus images are subjected to the pyramid histogram of oriented gradients (PHOG) and speeded up robust features (SURF) techniques. Then, the extracted data are subjected to adaptive synthetic sampling to balance the number of data in the two classes (normal and abnormal). Subsequently, we employed the canonical correlation analysis approach to fuse the highly-correlated features extracted from the two (PHOG and SURF) descriptors. We have achieved 96.21% accuracy, 95.00% sensitivity, and 97.42% specificity with ten-fold cross-validation strategy using k-nearest neighbor (kNN) classifier. This novel algorithm has high potential in the diagnosis of normal eyes during the mass eye screening session or in polyclinics quickly and reliably. Hence, the patients having abnormal eyes can be sent to the main hospitals which will reduce the workload for the ophthalmologists. [Figure not available: see fulltext.]

The current study was carried out in Baghdad-Iraq from March to November 2018 and included 110 participant (50 patients with diabetic retinopathy, 35 without diabetic retinopathy and 25 healthy control). Diabetic patients who attended who attended specialized center for Endocrinology and diabetes in Baghdad city, all diabetic retinopathy were diagnosis by specialist ophthalmologist, control group was from blood donors who haven’t any chronic disorders. Patient other ocular, patients with previous vitrectomy, other ocular surgery were excluded from the study. Blood sample were collected from each participant in the study for determination of HbA1c, Interleukin-12 and IFN- ? levels. The study demonstrated that 29 (58%) of patients with diabetes retinopathy were males compared with 21(42%) were females, 15(42.86%) of diabetes patients without retinopathy were males compared with 10(57.14%) were females, so mean age of diabetes retinopathy patients was 55.5±1.1 year while 42.8±2.1 year in diabetes without retinopathy (P

The results of morphological and functional research of macular zone of the retina in patients with diabetes mellitus (DM) and without diabetic retinopathy (DR) are presented. Analysis of tomographic and retinographic parameters revealed decrease of thickness and damage of functional activity of the macular zone.

Large-scale clinical trials, such as the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) studies, have shown that the administration of fenofibrate, a peroxisome proliferator-activated receptor alpha (PPAR&alpha, ) agonist, suppresses the progression of diabetic retinopathy. In this paper, we reveal a therapeutic effect of a selective PPAR&alpha, modulator (SPPARM&alpha, ), pemafibrate, against pathological angiogenesis in murine models of retinopathy. Oxygen-induced retinopathy (OIR) was induced in C57BL/6J mice by exposure to 85% oxygen from postnatal day eight (P8) for 72 h. Vehicle, pemafibrate or fenofibrate was administrated by oral gavage from P12 to P16 daily. Administration of pemafibrate, but not fenofibrate, significantly reduced pathological angiogenesis in OIR. After oral pemafibrate administration, expression levels of downstream PPAR&alpha, targets such as acyl-CoA oxidase 1 (Acox1), fatty acid binding protein 4 (Fabp4), and fibroblast growth factor 21 (Fgf21) were significantly increased in the liver but not in the retina. A significant increase in plasma FGF21 and reduced retinal hypoxia-inducible factor-1&alpha, (HIF-1&alpha, ) and vascular endothelial growth factor A (Vegfa) were also observed after this treatment. In an in vitro HIF-luciferase assay, a long-acting FGF21 analogue, but not pemafibrate, suppressed HIF activity. These data indicate that SPPARM&alpha, pemafibrate administration may prevent retinal pathological neovascularization by upregulating FGF21 in the liver.

Introduction: Diabetes retinopathy (DR) is the fourth leading cause for blindness worldwide. Screening is vital for its early detection and prevention of blindness. However screening all diabetic patients by specialists is impossible, particularly in resource poor settings such as Sri Lanka. We aimed to compare the agreement between non-specialist doctors and specialist ophthalmologists in diagnosing DR and planning follow up. Methods: A descriptive study was conducted with a cohort of patients randomly recruited from the Sri Lanka Young Diabetes Study (SLYDS). They were examined by a group of non-specialist doctors by direct ophthalmoscopy after mydriasis, and specialists (by slit lamp biomicroscopy) who were blinded to non-specialist doctors’ finding. Agreement between DR grading according to International Clinical Diabetic Retinopathy Disease Severity Scale, and follow up decisions, by non-specialist and specialist doctors were assessed with kappa statistic, using SPSS-16. Results: Our study included 658 participants (males 36%, mean age 37.1 years, mean duration of diabetes 5.22 years (± 4.04), mean HbA1c 8.1% (±2.04)) and 123 (18.7%) had diabetes retinopathy and 54 (8.3%) required early referral. Exact agreement between ophthalmologists and non specialist doctors in determining absence or presence of diabetes retinopathy was 0.82 (Kappa 0.48, p < 0.001) while the agreement in diagnosing the grade of retinopathy was 0.76 (Kappa 0.347, p < 0.001). Junior doctors detected DR with a sensitivity and specificity of 0.68 and 0.86 respectively. Exact agreement on follow up decision was 0.92 (Kappa 0.48, p < 0.001), with 0.52 sensitivity and 0.96 specificity in decision for early referral. Conclusion: Non-specialist doctors can identify DR with reasonable sensitivity. This should be encouraged in primary care, particularly in resource poor settings. Referring patients with any form of DR to a specialist should be recommended to prevent those with severe degrees of retinopathy from being missed for appropriate specialist care. DOI: http://dx.doi.org/10.4038/sjdem.v3i2.6363 Sri Lanka Journal of Diabetes, Endocrinology and Metabolism 2013; 3: 63-67

Diabetic microangiopathy, including retinopathy, is characterized by abnormal growth and leakage of small blood vessels, resulting in local edema and functional impairment of the depending tissues. Mechanisms leading to the impairment of microcirculation in diabetes are multiple and still largely unclear. However, a dysregulated vascular regeneration appears to play a key role. In addition, oxidative and hyperosmolar stress, as well as the activation of inflammatory pathways triggered by advanced glycation end-products and toll-like receptors, have been recognized as key underlying events. Here, we review recent knowledge on cellular and molecular pathways of microvascular disease in diabetes. We also highlight how new insights into pathogenic mechanisms of vascular damage in diabetes may indicate new targets for prevention and treatment.

Human eye is one of the most sophisticated organ, with retina, pupil, iris cornea, lens and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. DR is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of DR are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources used for mass screening of DR. We present an automatic screening system for the detection of normal and DR stages (NPDR and PDR). The proposed systems involves processing of fundus images for extraction of abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture and entropies. Our protocol uses total of 156 subjects consisting of two stages of DR and normal. In this work, we have fed thirteen statistically significant (p

Automatic diabetes retinopathy (DR) recognition can help DR carriers to receive treatment in early stages and avoid the risk of vision loss. In this paper, we emphasize the role of multiple filter sizes in learning fine-grained discriminant features and propose: (i) two deep convolutional neural networks - Combined Kernels with Multiple Losses Network (CKML Net) and VGGNet with Extra Kernel (VNXK), which are an improvement upon GoogLeNet and VGGNet in context of DR tasks. Learning from existing research, (ii) we propose a hybrid color space, LGI, for DR recognition via proposed nets. (iii) Transfer learning is applied to solve the challenge of imbalanced dataset. The effectiveness of proposed new nets and color space is evaluated using two grand challenge retina datasets: EyePACS and Messidor. Our experimental results show: (iv) CKML Net improves upon GoogLeNet and VNXK improves upon VGGNet on both datasets using the LGI color space. Additionally, proposed methodology improves upon other state of the art results on Messidor dataset for referable/non-referable screening.

Diabetes retinopathy is a retinal disease that is affected by diabetes on the eyes. The main risk of the disease can lead to blindness. Detection the disease at early stage can rescue the patients from loss of vision. The major purpose of this paper is to automatically detect as well as to classify the severity of diabetic retinopathy. At first, the lesions on the retina especially blood vessels, exudates and microaneurysms are extracted. Features such as area, perimeter and count from these lesions are used to classify the stages of the disease by applying artificial neural network (ANN). We used 214 fundus images from DIARECTDB1 and local databases. We found that the system can give the classification accuracy of 96% and it supports a great help to ophthalmologists.

Background To provide a normal database of choroidal thickness (CT) in nine Early Treatment Diabetes Retinopathy Study (ETDRS) subfields in Egypt using deep-range imaging swept source optical coherence tomography (DRI SS OCT). Methods This study included a total of 129 eyes of 71 normal Egyptian subjects, comprising 63 males and 66 females. The mean age was 36.85 ± 14.22 years (range, 16–67 years). The mean axial length was 23.84 ± 0.78 mm. CT was measured in nine subfields as defined by the ETDRS-style grid using a DRI SS OCT, and line measurements of subfoveal choroidal thicknesses (SFCT) were also performed. Results Mean SFCT was 300.87 ± 72.256 μm for ring measurements and 319.72 ± 76.45 μm for line measurements (P = 0.04). CT was higher in the superior and temporal quadrants than the inferior and nasal quadrants. A negative correlation between subfoveal choroidal thickness and age was detected in all regions (P

Objective: Diabetes retinopathy is one of the most common and earliest complications of diabetes mellitus. Blindness is 25 times more common at the diabetics than in common population and its incidence in Europe amounts to 60-65 at the population of 100.000 diabetics, yearly. The aim of this study is to analyse causality of the diabetes retinopathy with the sex, age, family predisposition, time of duration and type of the diabetes mellitus. Method: Research was conducted at the Institute for the the workers health care, in Kragujevac, for period from 15th November 2009. to 15th November 2010. and involved 135 patients. Experimental group had 90 patients with progressive diabetes retinopathy and the other, control one, involved 45 patientswith diabetes mellitus without progressive diabetes retinopathy. Results: In both examined groups men make two thirds of patients. Positive family predisposition was found in 68 patients (50,4%), from wich 47 (69,1%) were from experimental group. Most of patients were 51-60 years old, and average oldness is 57,5 years in control group and 60,1 years in experimental group. The average duration of diabetes in control group was 11,7 years and 14,4 years in experimental group. Conclusion: Researching show that the sex and family predisposition don't influence to the manifestation and progress of the diabetes retinopathy. The age of the patients also doesn't influence to the manifestation of this complication. Time of duration diabetes mellitus influences to the appearance of diabetes retinopathy. If the time of illness duration is prolonged for one year, chance for appearance diabetes retinopathy is increased for 1,068 time, more exactly 6,8%.

Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. Various image processing techniques have been used to identify the different stages of diabetes retinopathy. The application of non-linear features of the higher-order spectra (HOS) was found to be efficient as it is more suitable for the detection of shapes. The aim of this work is to automatically identify the normal, mild DR, moderate DR, severe DR and prolific DR. The parameters are extracted from the raw images using the HOS techniques and fed to the support vector machine (SVM) classifier. This paper presents classification of five kinds of eye classes using SVM classifier. Our protocol uses, 300 subjects consisting of five different kinds of eye disease conditions. We demonstrate a sensitivity of 82% for the classifier with the specificity of 88%.

Context: There is a lack of evidence on the subjective aspects of the provider perspective regarding diabetes and its complications in India. Objectives: The study was undertaken to understand the providers' perspective on the delivery of health services for diabetes and its complications, specifically the eye complications in India. Settings and Design: Hospitals providing diabetic services in government and private sectors were selected in 11 of the largest cities in India, based on geographical distribution and size. Methods: Fifty-nine semi-structured interviews conducted with physicians providing diabetes care were analyzed all interviews were recorded, transcribed, and translated. Nvivo 10 software was used to code the transcripts. Thematic analysis was conducted to analyze the data. Results: The results are presented as key themes: “Challenges in managing diabetes patients,” “Current patient management practices,” and “Strengthening diabetic retinopathy (DR) services at the health systems level.” Diabetes affects people early across the social classes. Self-management was identified as an important prerequisite in controlling diabetes and its complications. Awareness level of hospital staff on DR was low. Advances in medical technology have an important role in effective management of DR. A team approach is required to provide comprehensive diabetic care. Conclusions: Sight-threatening DR is an impending public health challenge that needs a concerted effort to tackle it. A streamlined, multi-dimensional approach where all the stakeholders cooperate is important to strengthening services dealing with DR in the existing health care setup.

Background The purpose of this study was to observe whether invisible morphological changes are presented in the two types of diabetes mellitus patients without diabetic retinopathy. Methods Twenty-six type 1 diabetes mellitus (T1DM) patients and 34 type 2 diabetes mellitus (T2DM) patients without diabetic retinopathy (DR) were recruited for this study. They underwent complete examinations that included stereoscopic color fundus photography and optical coherence tomography (OCT). The OCT patterns were used to measure the macular retinal thickness (RT), the ganglion cell and inner plexiform layer (GC-IPL) complex thickness, the inner nuclear layer (INL) thickness, the outer nuclear layer (ONL) thickness and the subfoveal choroidal thickness (SFCT) using the enhanced depth imaging (EDI) patterns and the retinal fiber layer (RNFL) thickness around the optic disc. All results were compared to those of age- and sex-matched control groups. Results In the patients with T1DM, the mean RT and GC-IPL complex thicknesses were significantly thinner than those of the control group (p

The aim of this study is to examine the association between cardiovascular risk profile, diabetes-specific factors, and prevalent retinopathy in sub-Saharan Africans with type 2 diabetes. We enrolled 213 (41 % women) diabetic adults from the Cameroon National Obesity Center in 2008. Prevalent retinopathy was based on history and digital mydriatic retinal photography. Cardiovascular risk profile was assessed, and global absolute coronary risk was estimated with the UK Prospective Diabetes Study (UKPDS) and Framingham coronary risk equations. Logistic regressions were used to relate risk factors to prevalent retinopathy and discrimination and calibration assessed. Fifty-four patients (25.4 %) had prevalent of retinopathy. Patients with retinopathy had more often hypertension (88 vs 43 %, p = 0.003), and age at diagnosis, known duration of diabetes, blood glucose, and systolic blood pressure were associated with retinopathy. Estimated 10-year coronary risks by the Framingham and UKPDS had low discrimination for retinopathy, with C-statistic (95 % confidence interval) of 0.593 (0.504–0.682) and 0.603 (0.518–0.688), respectively. New equations developed from this sample had better discrimination and calibration, with a maximum C-statistic of 0.881 (0.828–0.933). Cardiovascular risk profile was poorly correlated with prevalent retinopathy in this population. Strategies based upon routinely assessed diabetes-related factors can improve the selection of patients at high risk of retinopathy for costly confirmatory investigations.

OBJETIVO: Trabalho com o objetivo de identificar as alterações do pé diabético causadas pelas lesões microangiopáticas e das lesões do fundo de olho secundárias aretinopatia diabética. MÉTODOS: 76 pacientes com Diabetes Melito tipos 1 e 2atendidos no ambulatório de Oftalmologia e Cirurgia Vascular do HUOL/UFRN, Natal, RN, no período de novembro de 2004 a janeiro de 2005, com queixas relativas a alterações da retinopatia diabéticae/oudo pé diabético. Em todos os pacientes foi realizado exame clínico geral, vascular e oftalmológico. Na avaliação específicado pé diabético deu-se ênfase paraa investigação do status vascular pela Classificação de Fontaine para Doença Arterial Obstrutiva Periférica, biomecânica,e teste do monofilamento de Semmes-Weinstein. O exame oftalmológico constou de refração e fundoscopiaatravés da qual identificou-se as formas clínicas da retinopatia diabética. Os dados foram submetidos à análise estatística das variáveis primárias que consistiu em caracterizar o grupo quanto a idade, tempo de doença, nível de glicose A segunda estratégia da análise dos dados constituiu na realização de testes de associação entrealgumas variáveis secundárias selecionadas. O software utilizado para os testes estatísticos foi o Statistica Versão 5, 1997. RESULTADOS: Dos 76 pacientes diabéticos 97% tinham idade superior a 40 anos. O tempo de doença65% tinham mais de 10 anos. Com relação à glicose 72,72% apresentaram níveis de glicose em jejum acima de 100mg/dl. 55,26% apresentavam algum grau de retinopatia diabética contra 44,74% que não apresentavamesses sinais. Com as alterações do pé diabético, identificou-se 59,93% com lesões com área de predominância isquêmica, enquanto 41,07% tinham ausência de sinais. 58,82% apresentaram área de predominância neuropática, e 41,18% sem sinais de neuropatia. Dos com retinopatia diabética 78,57% tinham comprometimento isquêmico no pé e 47,62% tinham algum grau de neuropatia diabética. Observou-se que a retinopatia diabética não proliferativa, nos seus diversos graus de comprometimento apresentou-se com percentuais em torno de 80% junto às lesões do pé diabético, seja isquêmico ou neuropático. Dos pacientes que tinham retinopatia 60,46% tinham alterações biomecânicas dos pés. CONCLUSÃO: Concluiu-se que a RDNP leve foi mais freqüente nas lesões do pé diabético isquêmico, enquanto a RDNP severa mostrou-se mais presente no pé diabético neuropático. PURPOSE: To identify diabetic foot abnormal changes caused by microvascular events and fundoscopy eye lesions due to diabetic retinopathy. METHODS: A survey was performed with 76 diabetic patients from the Hospital Onofre Lopes out-patient department of ophtalmology and vascular surgery. To evaluate the diabetic foot the patients were submmited to an individual interview using Fontaine classification. The vascular test used was Semmes-Weinstein monofilament. Refraction and eye fundoscopy were acomplished in all patients to arrange the diabetic retinopathy.The study results consisted in characterized the group as age,time of disease and glicose level. The second analyse was performed with association tests among the selected secondary study results. " Statística Versão 5,1997 " was the software available. RESULTS: From 76 diabetics patients 97% had age higher than 40 years. 65% had more than 10 years of time disease. 72,72% obtained glicose level>100mg/dl. 55,5% had some degree of diabetic retinopathy against 44,74% had not. About the diabetic foot abnormalities, 59,93% had ischemic damages and 41,07% had not signs. 58,82% had neuropathic foot and there were 41,18% patients without diabetic neuropathy signs. Talking about the diabetic retinopathy population, 78,57% had ischemic foot and 47,62% had neuropathic foot. It was seen 80% of no proliferative diabetic retinopathy in all diabetics foot ( isquemic and neuropathic).The patients with retinopathy 60,46% of them had foot biomechanics abnormalities. CONCLUSION: Light no proliferative diabetic retinopathy was most common in patients with ischemic diabetic foot. The severe no proliferative diabetic retinopathy was most common in patients with neuropathic diabetic foot.

Blindness is one of the most feared complications of diabetes but also one of the most preventable. Diabetes is the commonest cause of blindness in people aged 30 to 69 years. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Even at the time of diagnosis of type 2 diabetes, about a quarter of patients have established background retinopathy. Treatment can now prevent blindness in the majority of cases, so it is essential to identify patients with retinopathy before their vision is affected.

Registration of retinal images provided by different modalities is required to facilitate diagnosis of the retina specifically in context with Diabetes Retinopathy (DR). Temporal registration is necessary in order to follow the various stages DR, whereas multimodal registration allows us to improve the identification of some lesions or to compare pieces of information gathered from different sources. This paper presents an algorithm for temporal and/or multimodal registration of retinal images based on directional gradient of salient vessel bifurcation points. Gradient along 0, +45 and 90 degrees around bifurcation point is invariant to rotation. We have validated this by using Fourier transform. This enables to estimate the correspondence of bifurcation points between the image pairs to be aligned. The retinal vessel tree is first segmented and vessel bifurcation points are located. A feature vector comprising of directional gradients along 0, +45 and 90 degrees around labeled bifurcation point in its 3 × 3 neighborhood is then computed in order to determine correspondence. The rotation is estimated from the coordinates of the matched vessel bifurcation points. The algorithm is tested on publically available DRIVE and STARE database. The overall registration error is observed to be 0.8% and 0.7% for STARE and DRIVE database respectively.