Retinal Databases Research Articles

Three-Year Clinical Outcomes in Geographic Atrophy: An Analysis of 18,712 Patient Eyes

Methods: Retrospective analysis of 18,712 eyes with GA using the CorEvitas Vestrum Health Retina Database. Results: Mean age at index was 78.6 years (SD = 7.9) and mean visual acuity (VA) was 67.5 letters (SD = 13.0, Snellen equivalent 20/45). 18.9% of eyes developed nAMD within 36 months. Eyes with fellow-eye nAMD developed nAMD at over twice the rate of eyes with fellow-eye GA (relative risk 2.34, 95% CI [2.20, 2.49]). Mean VA of eyes that did not develop nAMD declined by 12.4 letters (95% CI [12.0, 12.9]) within 36 months. Older age and moderate baseline visual impairment (VA < 20/40 to 20/100) independently correlated with accelerated rate of decline. Eyes of patients in the oldest quartile with moderate visual impairment experienced the worst outcomes, losing an average of 19.7 letters over 36 months (95% CI [18.1, 21.3]). By 36 months, 70% of eyes had vision below threshold for driving (VA ≤ 20/40), 42% had low vision (VA ≤ 20/70), and 23% were legally blind (VA ≤ 20/200). Conclusions: GA is associated with significant disease burden. Eyes with GA lose an average of two to three lines of VA within 36 months of follow up. Older age and moderate baseline visual impairment independently correlate with poorer visual outcomes. Presence of nAMD in the fellow eye is associated with twofold higher risk of exudative conversion within 36 months.

Retrospective Large Database Study of Central Serous Chorioretinopathy Treatments and Visual Outcomes Analysis in the United States.

Purpose: To evaluate a large database detailing the changes in visual acuity (VA) and central subfield thickness after various treatments for central serous chorioretinopathy (CSCR). Methods: A retrospective analysis was performed of patients with CSCR from January 2015 to September 2022 using the Vestrum Health Retina Database of aggregated de-identified electronic medical records from retina specialists in the United States. The cases of CSCR were categorized by age, sex, and treatment provided. Results: The annual incidence of CSCR was 1.72% (61 755 of 3 598 672 patient eyes), with a mean patient age of 53 years. Male eyes comprised 71.8% of the patient population. Eighty-five percent of patients received no treatment within 1 year of diagnosis. Of the patients needing treatment, 21% received thermal laser therapy, 23% photodynamic therapy, and 49% antivascular endothelial growth factor (anti-VEGF) intraocular injection. Patients not receiving treatment had the best baseline and 1-year VA. All treatment groups had an increased percentage of patients gaining letters compared with patients not receiving treatment. Conclusions: Although most patients did not require treatment, those who received treatment generally did well, with a large proportion having visual gain. Statistical analysis suggests treatment has a positive impact on VA outcomes. Patients receiving combination treatment were older and had the least visual gain of the treated cohorts. Younger patients with CSCR treated with anti-VEGF (and without a secondary diagnosis of macular degeneration) had the greatest increase in VA at 1 year.

Levodopa Is Associated with Reduced Development of Neovascular Age-Related Macular Degeneration

To determine whether levodopa (L-DOPA) is associated with a reduced likelihood of developing neovascular age-related macular degeneration (AMD). Three studies were performed: retrospective analyses in the Vestrum Health Retina Database (#1-2) and case-control analysis in the Merative MarketScan Research Databases (#3). Eyes with neovascular AMD and 2 years of follow-up (#1). Eyes with non-neovascular AMD and 1 to 5 years of follow-up (#2). Patients aged ≥ 55 years with newly diagnosed neovascular AMD matched to controls without neovascular AMD (#3). Eyes were divided into 2 groups (#1-2): exposed to L-DOPA before or on the date of neovascular (#1) or nonneovascular (#2) AMD diagnosis, and eyes not exposed to L-DOPA. We extracted AMD risk factors, number of intravitreal injections (#1), and conversion rate to neovascular AMD (#2). We calculated the percentage of newly diagnosed neovascular AMD cases and matched controls exposed to any L-DOPA and determined the cumulative 2-year dose in grams by tertiles (< 100 mg, approximately 100-300 mg, and approximately > 300 mg per day, #3). Number of intravitreal injections (#1) and detection of new-onset neovascular AMD (#2-3) after adjusting for AMD risk factors. In the Vestrum database, eyes with neovascular AMD that were exposed to L-DOPA underwent 1 fewer intravitreal injection over 2 years (N= 84 088 control vs. 530 L-DOPA eyes, P= 0.006). In eyes with nonneovascular AMD (N= 42 081-203 155 control vs. 314-1525 L-DOPA eyes), L-DOPA exposure was associated with a reduced risk of conversion to neovascular AMD by 21% at year 2 (P= 0.029), 35% at years 3 to 4 (P < 0.001), and 28% at year 5 (P= 0.024). In the MarketScan databases (N= 86 900 per group), cumulative 2-year doses of L-DOPA between approximately 100 to 300 mg per day and approximately > 300 mg were associated with decreased odds of developing neovascular AMD by 15% (odds ratio [OR], 0.85; 95% confidence interval [CI], 0.75-0.97) and 23% (OR, 0.77; 95% CI, 0.67-0.87), respectively. Levodopa use was associated with reduced detection of new-onset neovascular AMD. A prospective, randomized clinical trial should be considered to investigate whether low-dose L-DOPA reduces neovascular AMD conversion. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Evaluating the Effect of Hypoglycemic Agents on Diabetic Retinopathy Progression

Newer hypoglycemics such as dipeptidyl peptidase 4 (DPP-4) inhibitors, sodium-glucose cotransporter-2 (SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists have been increasingly used in diabetes. This study aimed to assess the relationship between usage of these hypoglycemic agents and effect on diabetic retinopathy (DR). Using the Vestrum Health Retina Database, patients with DR with 1 year follow-up after use of a hypoglycemic agent were included and stratified by agent, including no pharmacotherapy. Of 60,649 eyes, in 1 year after hypoglycemic agent usage, progression rates from severe nonproliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR) were the following: DPP-4 (17%), SGLT-2 (12%), GLP-1 (21%), metformin (18%), and none (20%). Progression rates from moderate NPDR to severe NPDR or PDR were the following: DPP-4 (11%), SGLT-2 (10%), GLP-1 (11%), metformin (10%), none (13%). Progression rates from mild NPDR to moderate/severe NPDR or PDR were the following: DPP-4 (6%), SGLT-2 (9%), GLP-1 (9%), metformin (7%), and none (10%). Within a large real-world database, patients prescribed GLP-1 agonists were found to have DR progression rates comparable to those of patients receiving no hypoglycemic agents. [Ophthalmic Surg Lasers Imaging Retina 2023; 54(3):158-165.].

Human retinal biometric recognition system based on multiple feature extraction

Retinal biometric trait is one of the safest and most secure biometric traits that can be used for personal identification and authentication. The poor contrast in captured retinal images initially requires the adoption of a preprocessing technique, especially when the vessel segmentation is required. As a result, an effective preprocessing technique is employed before the feature extraction process. The reliability of the segmentation depends on the consistency of the contrast across the images. Due to the complexity of the retinal vascular patterns, segmenting the vessels manually is a time-consuming task that also requires extensive training and abilities to distinguish the thinner blood vessel pixels from the background of the retinal images. We present a unique blood vessel segmentation technique based on Radial Chebyshev Moments (RCMs) shape descriptors. Moment-based shape descriptors are typically strong, concise, and simple to compute. They are also insensitive to the object’s scale, rotation, and translation. Furthermore, to eliminate the eye movement and to overcome the various lighting problems, the following multiple features are extracted in the feature extraction stage to accurately authenticate a person: statistical (gray-level) features, shape-based (RCM) features, and structural features, such as minutiae, bifurcation points, optical disc, and enclosed regions. The principal components analysis methodology is used to optimize the feature vector, and the target image is identified using the support vector machine classifier. This step helps to determine whether the test image belongs to the authorized person or not. RIDB and VARIA are the two standard retinal authentication databases and DRIVE and STARE are the two general retinal databases that are used for evaluating the performance of the system. The system yielded a high identification rate of 98%, 97.5%, 97.2% and 94.4% with average processing time of 0.987 s, 0.40 s, 1.262 s and 0.65 s, respectively. From the results, it is evident that the proposed method is more effective and efficient for authenticating individuals.

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter (BADWUMF). The classifier used here is to figure out whether the OCT image is a CSCR case or not. 150 images are checked for this research work (75 abnormal from Optical Coherence Tomography Image Retinal Database, in-house clinical database, and 75 normal images). This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be minimized. The total precision is 90 percent obtained from the Two Class Support Vector Machine (TCSVM) classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale (SNNWPB) classifier using the MATLAB 2019a program.

Multi-omics approach dissects cis-regulatory mechanisms underlying North Carolina macular dystrophy, a retinal enhanceropathy

North Carolina macular dystrophy (NCMD) is a rare autosomal-dominant disease affecting macular development. The disease is caused by non-coding single-nucleotide variants (SNVs) in two hotspot regions near PRDM13 and by duplications in two distinct chromosomal loci, overlapping DNase I hypersensitive sites near either PRDM13 or IRX1. To unravel the mechanisms by which these variants cause disease, we first established a genome-wide multi-omics retinal database, RegRet. Integration of UMI-4C profiles we generated on adult human retina then allowed fine-mapping of the interactions of the PRDM13 and IRX1 promoters and the identification of eighteen candidate cis-regulatory elements (cCREs), the activity of which was investigated by luciferase and Xenopus enhancer assays. Next, luciferase assays showed that the non-coding SNVs located in the two hotspot regions of PRDM13 affect cCRE activity, including two NCMD-associated non-coding SNVs that we identified herein. Interestingly, the cCRE containing one of these SNVs was shown to interact with the PRDM13 promoter, demonstrated invivo activity in Xenopus, and is active at the developmental stage when progenitor cells of thecentral retina exit mitosis, suggesting that this region is a PRDM13 enhancer. Finally, mining of single-cell transcriptional data of embryonic and adult retina revealed the highest expression of PRDM13 and IRX1 when amacrine cells start to synapse with retinal ganglion cells, supporting the hypothesis that altered PRDM13 or IRX1 expression impairs interactions between these cells during retinogenesis. Overall, this study provides insight into the cis-regulatory mechanisms of NCMD and supports that this condition is a retinal enhanceropathy.

Optical disk segmentation in human retina images with golden eagle optimizer

In ophthalmology, examination of the optic nerve and disc of the retina is a common way to diagnose a variety of eye diseases such as aging, diabetes, optic neuritis, glaucoma, papilledema, and ischemic optic neuropathy. This study presents the implementation of three combined methods based on the Golden Eagle algorithm, backup vector machine and geometrically active contour method and image segmentation. These methods have been tested using the well-known retina database benchmark, RIM-ONE. This study uses preprocessing based on the geometrically active Shannon contour guided by the Golden Eagle algorithm and post-processing based on regular spaced cross-section segmentation. The Golden Eagle method is more efficient than approaches such as the geometrically active contour and the SVM method. The proposed method, compared to other methods, shows higher mean values in terms of image similarity and statistical index. It is confirmed that the proposed Golden Eagle method could be used in the future to assess the clinical fit of retinal images.

Longer-Term Anti-VEGF Therapy Outcomes in Neovascular Age-Related Macular Degeneration, Diabetic Macular Edema, and Vein Occlusion-Related Macular Edema: Clinical Outcomes in 130 247 Eyes

The clinical practice visual acuity (VA) outcomes of anti-VEGF therapy for up to 5 years were assessed in patients with neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), branch retinal vein occlusion-related macular edema (BRVO-ME), and central retinal vein occlusion-related macular edema (CRVO-ME). A retrospective analysis was performed using the Vestrum Health Retina Database. Treatment-naive patients with nAMD, DME, BRVO-ME, or CRVO-ME who received anti-VEGF injections between 2014 and 2019 and had follow-up data for ≥12 months. Data on age, sex, the number of anti-VEGF treatments, and VA were analyzed. Mean VA change up to 3 years (BRVO-ME and CRVO-ME) and 5 years (nAMD and DME). At 1, 3, and 5 years, in 67 666, 21 305, and 5208 eyes with nAMD, after a mean of 7.6, 19.5, and 32 injections, there was a mean change of+3.1,-0.2, and-2.2 letters, respectively. At 1, 3, and 5 years, in 40 832, 7728, and 1192 eyes with DME, after a mean of 6.2, 15.4, and 26.0 injections, there was a mean change of+4.7,+3.3, and+3.1 letters, respectively. At 1 and 3 years, in 12 451 and 3027 eyes with BRVO-ME, after a mean of 7.1 and 18.2 injections, there was a mean change of+9.5 and+7.7 letters, respectively. At 1 and 3 years, in 9298 and 2264 eyes with CRVO-ME, after a mean of 7.3 and 18.8 injections, there was a mean change of+8.3 and+6.0 letters, respectively (P < 0.01 for all VA changes of > 1 letter). In all 4 conditions, the mean VA increased with the mean number of anti-VEGF injections, eyes with a baseline VA of 20/40 or better tended to lose VA, and eyes with progressively worse baseline VA experienced a progressively greater VA gain at 3 years. In practice, patients with nAMD, DME, BRVO-ME, and CRVO-ME showed limited visual outcomes, with patients with nAMD tending to lose VA at 3 and 5 years. Across all 4 disorders, the mean change in VA correlated with treatment intensity at 1, 3, and 5 years. Patients with better baseline VA are more vulnerable to vision loss.

Optic Disk Segmentation Using Histogram Analysis

In the field of disease diagnosis with ophthalmic aids, automatic segmentation of the retinal optic disc is required. The main challenge in OD segmentation is to determine the exact location of the OD and remove noise in the retinal image. This paper proposes a method for automatic optical disc segmentation on color retinal fundus images using histogram analysis. Based on the properties of the optical disk, where the optical disk tends to occupy a high intensity. This method has been applied to the Digital Retinal Database for Vessel Extraction (DRIVE)and MESSIDOR database. The experimental results show that the proposed automatic optical segmentation method has an accuracy of 55% for DRIVE dataset and 89% for MESSIDOR database

Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE Features.

Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes and elevations as retinal fundus images frequently have irregular illumination and are poorly contrasting. Manual detection of EX is a time-consuming process to diagnose a mass number of diabetic patients. In the domain of signal processing, both SIFT (scale-invariant feature transform) and SURF (speed-up robust feature) methods are predominant in scale-invariant location retrieval and have shown a range of advantages. But, when extended to medical images with corresponding weak contrast between reference features and neighboring areas, these methods cannot differentiate significant features. Considering these, in this paper, a novel method is proposed based on modified KAZE features, which is an emerging technique to extract feature points and extreme learning machine autoencoders(ELMAE) for robust and fast localization of the EX in fundus images. The main stages of the proposed method are pre-processing, OD localization, dimensionality reduction using ELMAE, and EX localization. The proposed method is evaluated based on the freely accessible retinal database DIARETDB0, DIARETDB1, e-Ophtha, MESSIDOR, and local retinal database collected from Silchar Medical College and Hospital(SMCH). The sensitivity, specificity, and accuracy obtained by the proposed method are 96.5%, 96.4%, and 97%, respectively, with the processing time of 3.19 seconds per image. The results of this study are satisfactory with state-of-the-art methods. The results indicate that the approach taken can detect EX with less processing time and accurately from the fundus images.

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

Image recognition and understanding is considered as a remarkable subfield of Artificial Intelligence (AI). In practice, retinal image data have high dimensionality leading to enormous size data. As the morphological retinal image datasets can be analyzed in an expansive and non-invasive way, AI more precisely Deep Learning (DL) methods are facilitating in developing intelligent retinal image analysis tools. The most recently developed DL technique, Convolutional Neural Network (CNN) showed remarkable efficiency in identifying, localizing, and quantifying the complex and hierarchical image features that are responsible for severe cardiovascular diseases. Different deep layered CNN architectures such as LeeNet, AlexNet, and ResNet have been developed exploiting CNN morphology. This wide variety of CNN structures can iteratively learn complex data structures of different datasets through supervised or unsupervised learning and perform exquisite analysis for feature recognition independently to diagnose threatening cardiovascular diseases. In modern ophthalmic practice, DL based automated methods are being used in retinopathy screening, grading, identifying, and quantifying the pathological features to employ further therapeutic approaches and offering a wide potentiality to get rid of ophthalmic system complexity. In this review, the recent advances of DL technologies in retinal image segmentation and feature extraction are extensively discussed. To accomplish this study the pertinent materials were extracted from different publicly available databases and online sources deploying the relevant keywords that includes retinal imaging, artificial intelligence, deep learning and retinal database. For the associated publications the reference lists of selected articles were further investigated.

Design of Cascaded CNN for Medical Image Super Resolution

The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.

Decision Support System Tool for Arabic Text Recognition

The National Center for Education Statistics study reported that 80% of students change their major or institution at least once before getting a degree, which requires a course equivalency process. This error-prone process varies among disciplines, institutions, regions, and countries and requires effort and time. Therefore, this study aims to overcome these issues by developing a decision support tool called TiMELY for automatic Arabic text recognition using artificial intelligence techniques. The developed tool can process a complete document analysis for several course descriptions in multiple file formats, such as Word, Text, Pages, JPEG, GIF, and JPG. We applied a comparative approach in selecting the highest score using three Arabic text extraction algorithms: term frequency-inverse document frequency measure algorithm, Cortical.io tool with Retina Database, and keyword extraction using word co-occurrence algorithm. The data repository consisted of 1000 datasets built from five different faculties at King Abdul-Aziz University and King Faisal University. It was followed by a discussion of the evaluation techniques using precision and recall measurements, which indicated that the keyword extraction using word co-occurrence algorithm scored 90% for the English language and 80% for the Arabic language in terms of the F1 measure that focuses on the linguistic relation between words.

Efficient graphical-processor-unit parallelization algorithm for computing Eigen values

Several leading-edge applications such as pathology detection, biometric identification, and face recognition are based mainly on blob and line detection. To address this problem, Eigen value computing has been commonly employed due to its accuracy and robustness. However, Eigen value computing requires a raised computational processing, intensive memory data access, and data overlapping, which involve higher execution times. To overcome these limitations, we propose in this paper a new parallel strategy to implement Eigen value computing using a graphics processing unit (GPU). Our contributions are (1) to optimize instruction scheduling to reduce the computation time, (2) to efficiently partition processing into blocks to increase the occupancy of streaming multiprocessors, (3) to provide efficient input data splitting on shared memory to benefit from its lower access time, and (4) to propose new data management of shared memory to avoid access memory conflict and reduce memory bank accesses. Experimental results show that our proposed GPU parallel strategy for Eigen value computing achieves speedups of 27 compared with a multithreaded implementation, of 16 compared with a predefined function in the OpenCV library, and of eight compared with a predefined function in the Cublas library, all of which are performed into a quad core multi-central-processing unit platform. Next, our parallel strategy is evaluated through an Eigen value-based method for retinal thick vessel segmentation, which is essential for detecting ocular pathologies. Eigen value computing is executed in 0.017 s when using Structured Analysis of the Retina database images. Accordingly, we achieved real-time thick retinal vessel segmentation with an average execution time of about 0.039 s.

Review on retinal blood vessel segmentation - an algorithmic perspective

Medical image processing has progressed in leaps and bounds with the advent of radical medical imaging modalities. Blood vessel segmentation from the retinal fundus image is very useful in the diagnosis of chronic vascular diseases, arteriosclerosis, diabetic retinopathy, hypertension, etc. This review paper aims to bring out the existing algorithms developed for the segmentation of vessels in the fundus. This paper covers various segmentation approaches categorised under template matching, multi-scale approach, region growing, active contour model and pattern recognition methods. Pattern recognition is further classified as unsupervised, supervised and deep learning approaches. Performance metrics such as accuracy, specificity, sensitivity, and area under the curve measures for these algorithms performed on the appropriate retinal databases are tabulated and discussed. Moreover, this paper discusses the impact of retinal blood vessel segmentation in screening cardiovascular and cerebrovascular diseases. Also, this paper recommends a universal blood vessel segmentation algorithm for the medical vasculature images.

Review on retinal blood vessel segmentation - an algorithmic perspective

Medical image processing has progressed in leaps and bounds with the advent of radical medical imaging modalities. Blood vessel segmentation from the retinal fundus image is very useful in the diagnosis of chronic vascular diseases, arteriosclerosis, diabetic retinopathy, hypertension, etc. This review paper aims to bring out the existing algorithms developed for the segmentation of vessels in the fundus. This paper covers various segmentation approaches categorised under template matching, multi-scale approach, region growing, active contour model and pattern recognition methods. Pattern recognition is further classified as unsupervised, supervised and deep learning approaches. Performance metrics such as accuracy, specificity, sensitivity, and area under the curve measures for these algorithms performed on the appropriate retinal databases are tabulated and discussed. Moreover, this paper discusses the impact of retinal blood vessel segmentation in screening cardiovascular and cerebrovascular diseases. Also, this paper recommends a universal blood vessel segmentation algorithm for the medical vasculature images.

Retinal Fundus Image Research for Diagnosis of Diabetic Maculopathy

Fundus images are valuable resources in diagnosis of retinal diseases. This paper proposes a computer-aided method based on various feature extraction techniques and support vector machines (SVM) for detection and classification of diabetic maculopathy (DM). DM, defined as retinopathy within one disc diameter of the centre of the macula, is a major cause of sight loss in diabetes. Here, we bring out a new approach to detect DM based on retinal fundus image features. During the first stage the input image is enhanced and the optic disc is masked to determine the presence of regions of foveal neighborhood. The second stage, deals with various feature extraction technique based on transform, shape and texture features. Extracted features are further categorized as healthy or affected images. Here we go for classification task using the RBF Support Vector Machine (SVM) classification, the techniques have been tested on retinal databases and these are compared with trained phase to categorize Healthy and DM images. This method can detect DM with a level accuracy on par with human retinal specialists

Recognition and Classification of Diabetic Retinopathy utilizing Digital Fundus Image with Hybrid Algorithms

Diabetic Retinopathy (Damage in Retina) is the most common threatening diabetic eye disease and cause leading vision loss and blindness. A patient with the diabetic disease needs to experience occasional screening of eye. To analysis, ophthalmologists may utilize fundus or retinal pictures of the patient gained from advanced fundus camera. However, if the symptoms are identified earlier and proper treatment is provided through regular screening and monitoring, the blindness or vision loss can be avoided. The present study is intended on developing an automatic system for the analysis of the retinal of fundus images by using image-processing techniques. So as to accelerate the procedure, the discovery of diabetic retinopathy image processing methods is utilized In this proposed study, the performance is evaluated using different segmentation algorithms and classifiers namely fuzzy c-means clustering, naïve Bayesian classifier, support vector machine to detect the diabetic retinopathy. The presentation of the strategy is assessed on the freely accessible retinal databases like DRIVE, STARE. The presentation of the retinal vessels on DRIVE database, sensitivity 100% and specificity 97.5% while for STARE database the sensitivity 99%, specificity 97%.The detection of accuracy can be defined with respect to expert physician hand-drawn and ground truths and the results are comparatively obtained and analyzed.

Visual Acuity Outcomes and Anti–Vascular Endothelial Growth Factor Therapy Intensity in Neovascular Age-Related Macular Degeneration Patients: A Real-World Analysis of 49 485 Eyes

This study assessed anti-vascular endothelial growth factor (VEGF) therapy intensity and its relationship with visual acuity (VA) change in real-world neovascular age-related macular degeneration (nAMD) patients. This retrospective analysis was performed on a large database of aggregated, longitudinal, de-identified electronic medical records from a geographically and demographically diverse sample of patients of United States retina specialists (Vestrum Health Retina Database). Treatment-naïve nAMD patients who underwent anti-VEGF injections between January 1, 2012, and October 31, 2016, were eligible if follow-up data were available before October 31,2017. Age, gender, anti-VEGF treatment type, number of treatments, and VA were extracted from the database. Mean VA change assessed at 1 year and stratified based on number of anti-VEGF injections received over 1 year. In this analysis, 49 485 eyes were included. The mean age was 80.9 years, and 64% were female. Mean baseline VA was 53.8 letters (Snellen equivalent, 20/80). At 1 year, after a mean of 7.3 anti-VEGF injections, there was a mean gain of 1 letter (0.95 letter; 95% confidence interval [CI] for change in VA,+0.77 to+1.13 letter; P < 0.001). When stratified by anti-VEGF agent, the mean VA changes were nearly identical at 1 year. There was a linear relationship between mean letters gained and mean number of injections, between 4 and 10 injections over 1 year, with 4 or fewer or 10 or more injections associated with loss of vision or a plateau, respectively. Greater mean 1-year change in VA also trended with worse baseline VA; those patients with better VA at presentation tended to be particularly vulnerable to vision loss. Those who received the fewest injections tended to be older and have worse baseline VA. Real-world nAMD patients receive fewer anti-VEGF injections and experience worse visualoutcomes compared with patients receiving fixed, frequent therapy in randomized controlled trials. Mean change in VA correlates with treatment intensity at 1 year, but with ceiling effects related to treatment intensity andbaseline VA. Older patients and those with poor baseline VA may be particularly prone to undertreatment.