Diabetic retinopathy screening: global and local perspective

Diabetes mellitus has become a global epidemic. It causes significant macrovascular complications such as coronary artery disease, peripheral artery disease, and stroke; as well as microvascular complications such as retinopathy, nephropathy, and neuropathy. Diabetic retinopathy is known to be the leading cause of blindness in the working-age population and may be asymptomatic until vision loss occurs. Screening for diabetic retinopathy has been shown to reduce blindness by timely detection and effective laser treatment. Diabetic retinopathy screening is being done worldwide either as a national screening programme or hospital-based project or as a community-based screening programme. In this article, we review different methods of screening including grading used to detect the severity of sight-threatening retinopathy and the newer screening methods. This review also includes the method of systematic screening being carried out in Hong Kong, a system that has helped to identify diabetic retinopathy among all attendees in public primary care clinics using a Hong Kong–wide public patients’ database.published_or_final_versio

Diabetic Macular Edema Grading Based on Deep Neural Networks

Diabetic Macular Edema (DME) is a major cause of vision loss in diabetes. Its early detection and treatment is therefore a vital task in management of diabetic retinopathy. In this paper, we propose a new featurelearning approach for grading the severity of DME using color retinal fundus images. An automated DME diagnosis system based on the proposed featurelearning approach is developed to help early diagnosis of the disease and thus averts (or delays) its progression. It utilizes the convolutional neural networks (CNNs) to identify and extract features of DME automatically without any kind of user intervention. The developed prototype was trained and assessed by using an existing MESSIDOR dataset of 1200 images. The obtained preliminary results showed accuracy of (88.8 %), sensitivity (74.7%) and specificity (96.5 %). These results compare favorably to state-of-the-art findings with the added benefit of an automatic feature-learning approach rather than a time-consuming handcrafted approach

Prevalence of age-related macular degeneration in old persons: Age, Gene/environment Susceptibility Reykjavik Study.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links field.PURPOSE: To describe the prevalence and signs of early and late age-related macular degeneration (AMD) in an old cohort. DESIGN: Population-based cohort study. PARTICIPANTS: We included 5272 persons aged ≥66 years, randomly sampled from the Reykjavik area. METHODS: Fundus images were taken through dilated pupils using a 45-degree digital camera and graded for drusen size, type, area, increased retinal pigment, retinal pigment epithelial depigmentation, neovascular lesions, and geographic atrophy (GA) using the modified Wisconsin Age-Related Maculopathy Grading System. MAIN OUTCOME MEASURES: Age-related macular degeneration in an elderly cohort. RESULTS: The mean age of participants was 76 years. The prevalence of early AMD was 12.4% (95% confidence interval [CI], 11.0-13.9) for those aged 66 to 74 years and 36% (95% CI, 30.9-41.1) for those aged ≥85 years. The prevalence of exudative AMD was 3.3% (95% CI, 2.8-3.8). The prevalence of pure GA was 2.4% (95% CI, 2.0-2.8). The highest prevalence of late AMD was among those aged ≥85 years: 11.4% (95% CI, 8.2-14.5) for exudative AMD and 7.6% (95% CI, 4.8-10.4) for pure GA. CONCLUSIONS: Persons aged ≥85 years have a 10-fold higher prevalence of late AMD than those aged 70 to 74 years. The high prevalence of late AMD in the oldest age group and expected increase of elderly people in the western world in coming years call for improved preventive measures and novel treatments.National Institutes of Health, National Institute on Ageing and the National Eye Institute Z01-EY00401 N01-AG-1-2100 IHA Icelandic Parliament University of Icelan

Automated Identification of Diabetic Retinopathy: A Survey

Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy

Abundant Lipid and Protein Components of Drusen

Drusen are extracellular lesions characteristic of aging and age-related maculopathy, a major retinal disease of the elderly. We determined the relative proportions of lipids and proteins in drusen capped with retinal pigment epithelium (RPE) and in RPE isolated from non-macular regions of 36 human retinas with grossly normal maculas obtained <6 hr after death.Druse pellets were examined by light and electron microscopy. Component proteins were extracted using novel methods for preserved tissues, separated, subjected to tryptic digestion and LC-MS(MS)(2) analysis using an ion trap mass spectrometer, and identified with reference to databases. Lipid classes were separated using thin layer chromatography and quantified by densitometry. Major druse components were esterified cholesterol (EC), phosphatidylcholine (PC), and protein (37.5+/-13.7, 36.9+/-12.9, and 43.0+/-11.5 ng/druse, respectively). Lipid-containing particles (median diameter, 77 nm) occupied 37-44% of druse volume. Major proteins include vitronectin, complement component 9, apoE, and clusterin, previously seen in drusen, and ATP synthase subunit beta, scavenger receptor B2, and retinol dehydrogenase 5, previously seen in RPE. Drusen and RPE had similar protein profiles, with higher intensities and greater variability in drusen. C8, part of the complement membrane attack complex, was localized in drusen by immunofluorescence.At least 40% of druse content is comprised by lipids dominated by EC and PC, 2 components that are potentially accounted for by just one pathway, the secretion of lipoproteins by RPE. Manipulating genes encoding apolipoprotein pathways would be a fruitful approach to producing drusen with high EC content in laboratory animals. Therapies that directly mitigate drusen should prepare for the substantial volume of neutral lipids. The catalog of major druse proteins is nearing completion

Studi Akurasi Karakteristik Retina sebagai Future Identification dengan Euclidean Distance Metrics

Penelitian ini menghasilkan sistem keamanan menggunakan biometrik, dengan menggunakan retina sebagai identitas pengenalan yang akurat, serta efektif untuk meningkatkan proses identifikasi pada retina dimasa depan (future identification). Hal ini sangat penting untuk menentukan keakuratan sifat biometrik apa yang paling baik di dalam proses mengidentifikasi di masa depan, sekaligus membangun suatu sistem aplikasi atau tools yang dapat digunakan untuk mengetahui karakteristik distance meterics untuk mengukur akurasi retina sebagai identitas dimasa depan (future identification). Penggunaan retina dapat menjadi salah satu alternatif identifikasi manusia  seperti  untuk  pengganti  PIN  ATM  Bank,  Paspor  dan bidang-bidang lain yang memerlukan tingkat keamanan tinggi atau mustahil untuk dapat dipalsukan. Hasil dari penelitian ini ialah berbentuk pengujian untuk membuktikan tingkat akurasi CBIR dengan menggunakan citra query dengan dibangun database sebanyak 5.000 citra retina. Metode yang akan digunakan dalam menentukan similarity dan identification dengan menggunakan fitur warna. Histogram warna untuk pencarian citra dikerjakan dengan mengitung jumlah koefisien DCT dari setiap warna. Hasil penelitian menunjukan bahwa akurasi algoritma mendekati nilai 90%, akurasi ini cukup bagus di bidang image retrieval.  Di lihat dari kecepatan proses retrieval juga cukup cepat dimana rata –rata kecepatan proses dengan menggunakan 2.000 citra digital adalah kurang dari 10 detik