Early Signs Of Diabetic Retinopathy Research Articles

Correlation between macular sensitivity and kidney function in patients with diabetes.

The aim of this study is to investigate the correlation between kidney function in patients with diabetes and macular sensitivity at central 10° using microperimetry. A cross-sectional study was carried out on 30 diabetic patients (58 eyes). A full-threshold microperimetry of the central 10° of retina (the macula) was performed on all subjects, consisting of 37 measurement points, using a 4-2 representation strategy. Macular sensitivity was expressed as the average threshold value in decibels for the entire field tested. The correlation between macular sensitivity and GFR, as well as microalbuminuria, blood glucose and HbA1c, was calculated using Pearson correlation rank. A significant positive correlation was observed between GFR and macular sensitivity among both male and female study subjects. For male participants, the correlation was measured to r([16]) = [.615], p = [<.007] and r([15]) = [.844], p = [<.001] for the left and right eyes, respectively. As for female participants, the correlation was r ([9]) = [.903], p = [<.001] and r([10]) = [.941], p = [<.001] for the left and right eyes, respectively. The correlation between macular sensitivity and the following variables was statistically insignificant: microalbuminuria, blood glucose, and HbA1c. Despite intact visual acuity measured on standard ophthalmic examination, patients with impaired kidney function had decreased macular sensitivity. This emphasizes the importance of microperimetry in preventative care and detection of early signs of diabetic retinopathy. Furthermore, we should consider the use of microperimetry as an auxiliary tool for monitoring kidney function in diabetics.

Digital histology of retinal microaneurysms as provided by dense B-scan (DART) OCTA: characteristics and clinical relevance in diabetic retinopathy.

Retinal microaneurysms (MAs) are among the earliest signs of diabetic retinopathy (DR) and can be classified in several subtypes by non-invasive multimodal retinal imaging. The main aim of the present study is to characterize retinal MAs perfusion properties and their blood flow network connectivity by means of Dense Automatic-RealTime (DART) OCTA technology, checking the relationship with the multimodal retinal imaging classification and testing the clinical impact of DART. A cross-sectional, observational study setting was chosen. Multimodal retinal imaging included confocal multicolour, OCT, OCTA and DART OCTA. We classified retinal MAs accordingly with the recently proposed multimodal retinal imaging classification and we tested the role of DART OCTA for detecting retinal MAs blood flow network connectivity. We also tested the relationship with clinical parameters. We included 206 retinal MAs of 36 DR eyes. We categorized retinal MAs as red (70; 34%), mixed (106; 51%) and green (30; 15%), corresponding to precise characteristics on structural OCT and both (regular) enface and DART OCTA images. The agreement between en-face and DART OCTA techniques for detecting MAs perfusion was very high (overall ICC 0.98; p < 0.01). However, DART OCTA provided clearer visualization than enface OCTA for detecting the blood flow network connectivity of retinal MAs, especially looking at the afferent and efferent MAs capillaries. Multimodal retinal imaging classification of retinal MAs provided significant correlations with DR duration, DR stage, and macular capillary non-perfusion. DART OCTA provided several new insights on retinal MAs characteristics and their blood flow network connectivity.

Optical imaging for diabetic retinopathy diagnosis and detection using ensemble models

Diabetes, characterized by heightened blood sugar levels, can lead to a condition called Diabetic Retinopathy (DR), which adversely impacts the eyes due to elevated blood sugar affecting the retinal blood vessels. The most common cause of blindness in diabetics is thought to be Diabetic Retinopathy (DR), particularly in working-age individuals living in poor nations. People with type 1 or type 2 diabetes may develop this illness, and the risk rises with the length of diabetes and inadequate blood sugar management. There are limits to traditional approaches for the early identification of diabetic retinopathy (DR). In order to diagnose diabetic retinopathy, a model based on Convolutional neural network (CNN) is used in a unique way in this research. The suggested model uses a number of deep learning (DL) models, such as VGG19, Resnet50, and InceptionV3, to extract features. After concatenation, these characteristics are sent through the CNN algorithm for classification. By combining the advantages of several models, ensemble approaches can be effective tools for detecting diabetic retinopathy and increase overall performance and resilience. Classification and image recognition are just a few of the tasks that may be accomplished with ensemble approaches like combination of VGG19,Inception V3 and Resnet 50 to achieve high accuracy. The proposed model is evaluated using a publicly accessible collection of fundus images.VGG19, ResNet50, and InceptionV3 differ in their neural network architectures, feature extraction capabilities, object detection methods, and approaches to retinal delineation. VGG19 may excel in capturing fine details, ResNet50 in recognizing complex patterns, and InceptionV3 in efficiently capturing multi-scale features. Their combined use in an ensemble approach can provide a comprehensive analysis of retinal images, aiding in the delineation of retinal regions and identification of abnormalities associated with diabetic retinopathy. For instance, micro aneurysms, the earliest signs of DR, often require precise detection of subtle vascular abnormalities. VGG19′s proficiency in capturing fine details allows for the identification of these minute changes in retinal morphology. On the other hand, ResNet50′s strength lies in recognizing intricate patterns, making it effective in detecting neoneovascularization and complex haemorrhagic lesions. Meanwhile, InceptionV3′s multi-scale feature extraction enables comprehensive analysis, crucial for assessing macular oedema and ischaemic changes across different retinal layers.

Comparative Analysis of Diabetic Retinopathy Using Deep Learning Concepts

Diabetic retinopathy is a complication of diabetes that affects the eyes. It is a progressive disease that occurs in stages, each of which is characterized by specific changes in the retina. It is caused by damage to the blood vessels in the retina, which is the light-sensitive part of the eye that sends visual signals to the brain. People with diabetes, especially those with poorly controlled blood sugar levels, are at risk of developing diabetic retinopathy. Over time, high blood sugar levels can cause the blood vessels in the retina to become damaged or blocked. This can lead to swelling, bleeding, and the growth of abnormal blood vessels in the retina, which can cause vision loss or even blindness. Symptoms of diabetic retinopathy may include blurry vision, floaters (spots in the field of vision), and difficulty seeing at night. However, many people with diabetic retinopathy may not have any symptoms at all in the early stages of the condition. Treatment for diabetic retinopathy may involve controlling blood sugar levels, blood pressure, and cholesterol levels, as well as laser treatment, injections, or surgery in more advanced cases. Regular eye exams are also recommended for people with diabetes to detect any early signs of diabetic retinopathy and prevent vision loss. Keywords: Diabetic Retinopathy; Deep Learning; Image processing; Convolution neural network

Network pharmacology and biochemical experiments reveal the antiapoptotic mechanism of huperzine A for treating diabetic retinopathy

Background/aimsDiabetic retinopathy is the most common eye disease that causes blindness in the working population. Neurodegeneration is the early sign of diabetic retinopathy, but no drug has been approved for...

Segmentation of retinal microaneurysms in fluorescein fundus angiography images by a novel three-step model.

Microaneurysms serve as early signs of diabetic retinopathy, and their accurate detection is critical for effective treatment. Due to their low contrast and similarity to retinal vessels, distinguishing microaneurysms from background noise and retinal vessels in fluorescein fundus angiography (FFA) images poses a significant challenge. We present a model for automatic detection of microaneurysms. FFA images were pre-processed using Top-hat transformation, Gray-stretching, and Gaussian filter techniques to eliminate noise. The candidate microaneurysms were coarsely segmented using an improved matched filter algorithm. Real microaneurysms were segmented by a morphological strategy. To evaluate the segmentation performance, our proposed model was compared against other models, including Otsu's method, Region Growing, Global Threshold, Matched Filter, Fuzzy c-means, and K-means, using both self-constructed and publicly available datasets. Performance metrics such as accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union were calculated. The proposed model outperforms other models in terms of accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union. The segmentation results obtained with our model closely align with benchmark standard. Our model demonstrates significant advantages for microaneurysm segmentation in FFA images and holds promise for clinical application in the diagnosis of diabetic retinopathy. The proposed model offers a robust and accurate approach to microaneurysm detection, outperforming existing methods and demonstrating potential for clinical application in the effective treatment of diabetic retinopathy.

Early Assessment of Diabetic Retinopathy by Detecting Microaneurysm with Deep Neural Network

Objectives: This study aims to detect early signs of diabetic retinopathy, also known as microaneurysm (MA). Early detection of MA can help in diagnosing diabetic retinopathy effectively. Methods: To achieve this objective a method is proposed which is based on a deep learning model that incorporates transfer learning. The dataset used in this study is E-Ophtha which consists of 381 high-quality images. The proposed model consists of three steps which are preprocessing, feature extraction and classification. The method uses CLAHE to enhance the details of the fundus image. The feature extraction step and the classification are done using a deep learning algorithm. The proposed method is compared by using parameters such as Recall, Precision, and accuracy. Findings: The accuracy achieved by the model is about 98.82%-100%, a recall/sensitivity of 100%, and precision is also 100% which is close to the state of the art. This implies that deep learning methods are a good fit for identifying microaneurysms and ultimately detecting diabetic retinopathy. Novelty: Early detection and diagnosis of diabetic retinopathy can be achieved with this approach, and appropriate medication can impede the disease's development. Keywords: Diabetic Retinopathy, PDR, NPDR, Image Processing, Deep Learning

The single-cell landscape of alternative transcription start sites of diabetic retina

More than half of mammalian protein-coding genes have multiple transcription start sites. Alternative transcription start site (TSS) modulate mRNA stability, localization, and translation efficiency on post-transcription level, and even generate novel protein isoforms. However, differential TSS usage among cell types in healthy and diabetic retina remains poorly characterized. In this study, by using 5′-tag-based single-cell RNA sequencing, we identified cell type-specific alternative TSS events and key transcription factors for each of retinal cell types. We observed that lengthening of 5′- UTRs in retinal cell types are enriched for multiple RNA binding protein binding sites, including splicing regulators Rbfox1/2/3 and Nova1. Furthermore, by comparing TSS expression between healthy and diabetic retina, we identified elevated apoptosis signal in Müller glia and microglia, which can be served as a putative early sign of diabetic retinopathy. By measuring 5′UTR isoforms in retinal single-cell dataset, our work provides a comprehensive panorama of alternative TSS and its potential consequence related to post-transcriptional regulation. We anticipate our assay can not only provide insights into cellular heterogeneity driven by transcriptional initiation, but also open up the perspectives for identification of novel diagnostic indexes for diabetic retinopathy.

Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images

This paper presents a framework for the automated detection of Exudates, an early sign of Diabetic Retinopathy. The paper introduces a classification-extraction-superimposition (CES) mechanism for enabling the generation of representative exudate samples based on limited open-source samples. The paper demonstrates how the manipulation of Yolov5M output vector can be utilized for exudate extraction and super-imposition, segueing into the development of a custom CNN architecture focused on exudate classification in retinal based fundus images. The performance of the proposed architecture is compared with various state-of-the-art image classification architectures on a wide range of metrics, including the simulation of post deployment inference statistics. A self-label mechanism is presented, endorsing the high performance of the developed architecture, achieving 100% on the test dataset.

Microaneurysms detection in retinal images using a multi-scale approach

One of the earliest signs of diabetic retinopathy is the appearance of small and round red dots in retinal fundus images, known as microaneurysms. Microaneurysms automatic detection is a challenging task due to their small and subtle nature. In this paper, a new multi-scale algorithm developed for the automatic detection of microaneurysms in retinal fundus images is proposed. The proposed method is divided into three stages, namely, image pre-processing, microaneurysm candidates detection and retinal vessels detection, and labelling of the microaneurysm candidates. Apart the initial image pre-processing, all stages of the algorithm are designed using a multi-scale framework, providing the mechanism for the effective microaneurysms detection. Four publicly available databases are used for performance evaluation. The proposed method proves to be competitive against other state-of-the-art approaches in microaneurysms detection and diabetic retinopathy detection.

Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization.

Diabetic retinopathy (DR) is one of the most important microvascular complications associated with diabetes mellitus. The early signs of DR are microaneurysms, which can lead to complete vision loss. The detection of DR at an early stage can help to avoid non-reversible blindness. To do this, we incorporated fuzzy logic techniques into digital image processing to conduct effective detection. The digital fundus images were segmented using particle swarm optimization to identify microaneurysms. The particle swarm optimization clustering combined the membership functions by grouping the high similarity data into clusters. Model testing was conducted on the publicly available dataset called DIARETDB0, and image segmentation was done by probability-based (PBPSO) clustering algorithms. Different fuzzy models were applied and the outcomes were compared with our probability discrete particle swarm optimization algorithm. The results revealed that the proposed PSO algorithm achieved an accuracy of 99.9% in the early detection of DR.

Automated Detection and Counting of Hard Exudates for Diabetic Retinopathy by using Watershed and Double Top-Bottom Hat Filtering Algorithm

Diabetic Retinopathy (DR) is one of diabetes complications that affects our eyes. Hard Exudate (HE) are known to be the early signs of DR that potentially lead to blindness. Detection of DR automatically is a complicated job since the size of HE is very small. Besides, our community nowadays lack awareness on diabetic where they do not know that diabetes can affect eyes and lead to blindness if regular check-up is not performed. Hence, automated detection of HE known as Eye Retinal Imaging System (EyRis) was created to focus on detecting the HE based on fundus image. The purpose of this system development is for early detection of the symptoms based on retina images captured using fundus camera. Through the captured retina image, we can clearly detect the symptoms that lead to DR. In this study, proposed Watershed segmentation method for detecting HE in fundus images. Top-Hat and Bottom-Hat were use as enhancement technique to improve the quality of the image. This method was tested on 15 retinal images from the Universiti Sains Malaysia Hospital (HUSM) at three different stages: Normal, NPDR, and PDR. Ten of these images have abnormalities, while the rest are normal retinal images. The evaluation of the segmentation images would be compared by Sensitivity, F-score and accuracy based on medical expert's hand drawn ground truth. The results achieve accuracy 0.96 percent with 0.99 percent sensitivity for retinal images.

Effects of Quality Improvement Education in Diabetic Retinopathy on Routine Clinical Practice Patterns of Optometrists.

Optometrists play a preventive role in diabetic care by detecting early signs of diabetic retinopathy (DR), a leading cause of blindness in adults. This study demonstrates that additional training can improve optometrists' ability to assess the presence and severity of DR in individuals with diabetes. This study aimed to determine the impact of a quality improvement intervention involving education, assessment, and feedback on improving the evaluation and referral patterns of optometrists with regard to their patients with diabetes. A pre-interventional and post-interventional analysis of optometrist practices was conducted through a retrospective chart review of diabetic patient encounters from July 2018 to March 2019. Dilated fundus examination (DFE) documentation, follow-up scheduling, referral practices, and usage of various imaging modalities were collected from patient records. Concordance of DR severity recordings between DFE findings, assessment and plan notes, and International Classification of Diseases codes was calculated. After intervention, the proportion of optometrists who conducted a DFE significantly increased from 79.5 (95% confidence interval [CI], 77.3 to 81.7%) to 84.4% (95% CI, 82.4 to 86.4%). In addition, the rate of improper follow-up instructions decreased from 13.8 (95% CI, 12.0 to 15.7%) to 10.8% (95% CI, 9.2 to 12.6%), and the decrease was significant (P = .02). Although overall referrals decreased from 19.8 (95% CI, 17.6 to 21.9%) to 14.6% (95% CI, 12.6 to 16.5%), optometrists were as likely to refer to retinal specialists. Finally, concordance between documented DFE findings, assessment and plan notes, and International Classification of Diseases codes significantly increased from 78.8 (95% CI, 76.5 to 81.0%) to 88.7% (95% CI, 86.9 to 90.4%). Providing optometrists education in screening and assessing DR is effective in improving diabetic patient care.

Early Retinal Changes by OCT Angiography and Multifocal Electroretinography in Diabetes.

Background: To evaluate the earliest retinal morphological and functional changes in diabetic eyes without or with early signs of diabetic retinopathy (DR). Methods: Twenty-two eyes with no DR (noDR group), 22 eyes with mild DR (DR group), and 18 healthy nondiabetic eyes (controls) were enrolled. All eyes were studied by means of spectral domain optical coherence tomography (OCT), OCT angiography (OCTA), and multifocal electroretinogram (mfERG). Results: A significantly higher number of OCT hyperreflective intraretinal foci (HRF) was found in both noDR and DR groups versus controls, but not between DR groups. The OCTA parameters of the superficial vascular plexus (SVP) were significantly reduced in the noDR group both versus controls and DR group (p < 0.05). The OCTA parameters of the intermediate capillary plexus (ICP) were significantly reduced in the DR group versus controls. An increased number of altered hexagons on mfERG was found in the noDR versus the DR group (p = 0.0192). Conclusions: Retinal vascular and functional parameters are differently involved in diabetic eyes; major vascular changes in the SVP and functional alterations of the mfERG are present in diabetic eyes with no clinical microvascular signs of DR, while ICP is mainly involved when early ophthalmoscopic signs of DR are present. The integrated use of mfERG and OCTA provides new significant insights into the pathogenesis of diabetic related retinal disease.

Knowledge based Expert System for Predicting Diabetic Retinopathy using Machine Learning Algorithms

Diabetic retinopathy (DR) is a medical condition that can affect the patient's retina and cause leaks in the blood due to diabetes mellitus. The increase in cases of diabetes limits existing manual testing capability. Today new algorithms are becoming very important for assisted diagnosis. Effective diabetes diagnosis can benefit the victims and reduce the negative harmful effects, including blindness. If not treated in a timely manner, this disorder can cause different symptoms from mild vision problems to total blindness. Early signs of DR are the hemorrhages, hard exudates, and micro-aneurysms (HEM) that occur in the retina. Timely diagnosis of HEM is important for avoiding blindness This paper presents PSO feature selection algorithms with three classifications for the detection of Diabetic retinopathy using python.

Knowledge based Expert System for Predicting Diabetic Retinopathy using Machine Learning Algorithms

Diabetic retinopathy (DR) is a medical condition that can affect the patient's retina and cause leaks in the blood due to diabetes mellitus. The increase in cases of diabetes limits existing manual testing capability. Today new algorithms are becoming very important for assisted diagnosis. Effective diabetes diagnosis can benefit the victims and reduce the negative harmful effects, including blindness. If not treated in a timely manner, this disorder can cause different symptoms from mild vision problems to total blindness. Early signs of DR are the hemorrhages, hard exudates, and micro-aneurysms (HEM) that occur in the retina. Timely diagnosis of HEM is important for avoiding blindness This paper presents PSO feature selection algorithms with three classifications for the detection of Diabetic retinopathy using python.

MultiColor imaging to detect different subtypes of retinal microaneurysms in diabetic retinopathy.

Retinal microaneurysms (MAs) are among the earliest signs of diabetic retinopathy (DR) and are typically detected by fluorescein angiography (FA). Confocal MultiColor is a noninvasive-imaging technique able to analyze different retinal features by capturing three simultaneous reflectance images. The main aim of the present study was to characterize morphological features of MAs by means of MultiColor images and to compare these with spectral domain optical coherence tomography (SD-OCT) and FA findings. A cross-sectional, observational study setting was chosen. Multimodal imaging included MultiColor, SD-OCT and FA images. We performed a qualitative analysis in order to assess the relationship between MultiColor and its green- and red-reflectance components, SD-OCT (hyperreflective, hyporeflective and mixed reflectivity) and FA findings. MAs detected on our MultiColor images were then categorized in accordance with a previously published histological classification. In our study FA images were used to detect 153 MAs in 30 eyes displaying DR. MultiColor was able to distinguish 122 MAs (80%). We identified green (16%), red (19%), and mixed (65%) MAs, corresponding to different reflectivity features detected by SD-OCT. MAs not visualized on MultiColor images corresponded to tiny hyperreflective lesions on SD-OCT. We compared our imaging findings with a histological MA classification reported in the literature. Our findings showed a strict relationship between MA subtypes and SD-OCT, suggesting that the composition of MAs (cells + endothelium + fibrosis) may influence the signal detected in MultiColor images. MultiColor appears to be a useful technique for investigating MA features in patients with DR.

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images.

Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.

Diabetic retinopathy detection using red lesion localization and convolutional neural networks

Detecting the early signs of diabetic retinopathy (DR) is essential, as timely treatment might reduce or even prevent vision loss. Moreover, automatically localizing the regions of the retinal image that might contain lesions can favorably assist specialists in the task of detection.In this study, we designed a lesion localization model using a deep network patch-based approach. Our goal was to reduce the complexity of the model while improving its performance. For this purpose, we designed an efficient procedure (including two convolutional neural network models) for selecting the training patches, such that the challenging examples would be given special attention during the training process. Using the labeling of the region, a DR decision can be given to the initial image, without the need for special training.The model is trained on the Standard Diabetic Retinopathy Database, Calibration Level 1 (DIARETDB1) database and is tested on several databases (including Messidor) without any further adaptation. It reaches an area under the receiver operating characteristic curve of 0.912−95%CI(0.897−0.928) for DR screening, and a sensitivity of 0.940−95%CI(0.921−0.959). These values are competitive with other state-of-the-art approaches.

An automated early diabetic retinopathy detection through improved blood vessel and optic disc segmentation

This paper presents an automated early diabetic retinopathy detection scheme from color fundus images through improved segmentation strategies for optic disc and blood vessels. The red lesions, microaneurysms and hemorrhages are the earliest signs of diabetic retinopathy. This paper essentially proposes improved techniques for microaneurysm as well as hemorrhages detection, which eventually contribute in the overall improvement in the early detection of diabetic retinopathy. The proposed method consists of five stages- pre-processing, detection of blood vessels, segmentation of optic disc, localization of fovea, feature extraction and classification. Mathematical morphology operation is used for pre-processing and blood vessel detection. Watershed transform is used for optic disc segmentation. The main contribution of this model is to propose an improved blood vessel and optic disc segmentation methods. Radial basis function neural network is used for classification of the diseases. The parameters of radial basis function neural network are trained by the features of microaneurysm and hemorrhages. The accuracy of the proposed algorithm is evaluated based on sensitivity and specificity, which are 87% and 93% respectively.