Diagnosis Of Diabetic Retinopathy Research Articles

Development and Application of a Standardized Testset for an Artificial Intelligence Medical Device Intended for the Computer-Aided Diagnosis of Diabetic Retinopathy.

To explore a centralized approach to build test sets and assess the performance of an artificial intelligence medical device (AIMD) which is intended for computer-aided diagnosis of diabetic retinopathy (DR). A framework was proposed to conduct data collection, data curation, and annotation. Deidentified colour fundus photographs were collected from 11 partner hospitals with raw labels. Photographs with sensitive information or authenticity issues were excluded during vetting. A team of annotators was recruited through qualification examinations and trained. The annotation process included three steps: initial annotation, review, and arbitration. The annotated data then composed a standardized test set, which was further imported to algorithms under test (AUT) from different developers. The algorithm outputs were compared with the final annotation results (reference standard). The test set consists of 6327 digital colour fundus photographs. The final labels include 5 stages of DR and non-DR, as well as other ocular diseases and photographs with unacceptable quality. The Fleiss Kappa was 0.75 among the annotators. The Cohen's kappa between raw labels and final labels is 0.5. Using this test set, five AUTs were tested and compared quantitatively. The metrics include accuracy, sensitivity, and specificity. The AUTs showed inhomogeneous capabilities to classify different types of fundus photographs. This article demonstrated a workflow to build standardized test sets and conduct algorithm testing of the AIMD for computer-aided diagnosis of diabetic retinopathy. It may provide a reference to develop technical standards that promote product verification and quality control, improving the comparability of products.

Supervised learning software model for the diagnosis of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of eye diseases and vision loss for diabetic affected people. Due to the damage of retinal blood vessels, diabetic patients often suffer from DR. So the retinal blood vessel segmentation plays a crucial role in the diagnosis of DR. We can prevent vision loss or blindness problems if the diagnosis happens during the early stages. Early diagnosis and initial investigation would help lower the risk of vision loss by 50%. This article exploits the supervised classification approach to detect blood vessels by applying features such as grey level and invariant moments. The image pre-processing and blood vessel segmentation are the two essential steps are used in this study, along with the proposed classification framework using neural network models. Two publicly available retinal image datasets, such as DRIVE and STARE, are used to assess the proposed supervised classification framework. The suggested supervised classification methodology in this study attains the average retinal blood vessel segmentation accuracy of 93.94% in the DRIVE dataset and 95.00% in the STARE dataset.

Diabetic Retinopathy Grading From Color Fundus Images: An Autotuned Deep Learning Approach

Diabetic Retinopathy (DR) is a consequence of long-term diabetes which affects the eyes. It causes affliction to the veins in the eyes causing raptures on the retina which can impact vision. If the condition is failed to be detected at an early stage, it can lead to complete vision loss. The conventional diagnosing cycle of DR using fundus images requires very skilled practitioners due to the minute nature of features of the anomalies which can even lead to misdiagnosis and is time consuming. Therefore, devising an automated method for the diagnosis of DR can assist individuals with diabetes to identify symptoms of DR at a very early stage. This research also encompasses the classification of the detected images into its corresponding 3 stages namely No Diabetic Retinopathy (No DR), Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) that can greatly aid in monitoring the dynamics of the key features including lesions, hemorrhages, and density of blood vessels. Deep learning algorithms has gotten to become a well-known method that can accomplish a wide variety of classification tasks. However, most of these methods are only efficient in classifying the various stages of DR with low accuracies notably, for the early stages. The devised algorithm in this research uses enriched images processing techniques, automatic hyperparameter tuning and neural network training strategies to provide more emphasis on the minute features for better prediction. The algorithm was tested and compared with modified Resnet50, VGG16, Mobilenetv2, Inceptionv3 and InceptionResnetv2 which gave a classification accuracy of 94.7%, 86.1%, 85.8%, 85.3% and 87% respectively with corresponding detection accuracies of 99.8%, 94%, 94.2%, 94.9% and 98.2% respectively on a test set of 508 images. Using the proposed algorithm, the results indicate Resnet50 based network gave superior performance for both detection and classification tasks.

Automated hard exudate segmentation using neural encoders and attention mechanisms for diabetic retinopathy diagnosis

Automated hard exudate segmentation using neural encoders and attention mechanisms for diabetic retinopathy diagnosis

Enhanced retinal image captioning system for identifying and differentiating severity levels of diabetic retinopathy

The objective of this research was to formulate a clinical decision support framework leveraging AI towards utilizing retinal fundus images for the identification and categorization of the four distinct stages of diabetic retinopathy, namely proliferative, severe, moderate, and mild. The devised system architecture integrated Long Short-Term Networks (LSTM), Generative Adversarial Networks (GAN), and pre-trained convolutional neural network (CNN) models. Following an exhaustive performance analysis, the most optimal image captioning model was identified and recommended to ophthalmologists for the purpose of identifying and categorizing diabetic retinopathy. Notably, the results revealed that employing ResNet50 with LSTM, in conjunction with enhanced retinal images, yielded superior accuracy of 0.975. The proposed methodology holds transformative potential for the realm of diabetic retinopathy diagnosis and classification, facilitating early detection and intervention to mitigate vision loss in individuals affected by diabetes.

Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy, which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly, training for Convolutional Neural Networks is more time-consuming. This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model for timely DR identification that is potentially more accurate than existing CNN-based models. The proposed model will detect various lesions from retinal images in the early stages. First, characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification. For dimensionality reduction, EDLM is used. Additionally, the classification and feature extraction processes are optimized using the stochastic gradient descent optimizer. The EDLM effectiveness is assessed on the KAG GLE dataset with 3459 retinal images, and results are compared over VGG16, VGG19, RESNET18, RESNET34, and RESNET50.

Blood Vessel Segmentation with Classification Model for Diabetic Retinopathy Screening

Biomedical image processing is finding useful in healthcare sector for the investigation, enhancement, and display of images gathered by distinct imaging technologies. Diabetic retinopathy (DR) is an illness caused by diabetes complications and leads to irreversible injury to the retina blood vessels. Retinal vessel segmentation techniques are a basic element of automated retinal disease screening system. In this view, this study presents a novel blood vessel segmentation with deep learning based classification (BVS-DLC) model for DR diagnosis using retinal fundus images. The proposed BVS-DLC model involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. Primarily, the proposed model uses the median filtering (MF) technique to remove the noise that exists in the image. In addition, a multilevel thresholding based blood vessel segmentation process using seagull optimization (SGO) with Kapur’s entropy is performed. Moreover, the shark optimization algorithm (SOA) with Capsule Networks (CapsNet) model with softmax layer is employed for DR detection and classification. A wide range of simulations was performed on the MESSIDOR dataset and the results are investigated interms of different measures. The simulation results ensured the better performance of the proposed model compared to other existing techniques interms of sensitivity, specificity, receiver operating characteristic (ROC) curve, accuracy, and F-score.

A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images

Diabetic retinopathy (DR) is a common complication of diabetes mellitus, and retinal blood vessel damage can lead to vision loss and blindness if not recognized at an early stage. Manual DR detection using large fundus image data is time-consuming and error-prone. An effective automatic DR detection system can be significantly faster and potentially more accurate. This study aims to classify fundus images into five DR classes, using deep learning methods, with the highest possible accuracy and the lowest possible computational time. Three distinct DR datasets, APTOS, Messidor2, and IDRiD, are merged, resulting in 5,819 raw images. Before training the model, various image preprocessing techniques are applied to remove artifacts and noise from the images and improve their quality. Three augmentation techniques: geometric, photometric, and elastic deformation, are used to create a balanced dataset. A shallow convolutional neural network (CNN) is developed using three blocks of convolutional layers and maxpool layers with a categorical cross-entropy loss function, Adam optimizer, 0.0001 learning rate, and 64 batch size as a base model, and this is also employed to determine the best data augmentation method for further processing. A study to optimize the performance is then conducted by changing different components and hyperparameters of the base model, resulting in our proposed RetNet-10 model. Six cutting-edge models are employed for comparison. Our proposed RetNet-10 model performed the best, with a testing accuracy of 98.65%. MobileNetV2, VGG16, Xception, VGG19, InceptionV3 and ResNet50 achieved testing accuracies of 91.42%, 90.16%,89.57%, 88.21%, 87.68% and 87.23%, respectively. The model is also trained with several k values to assess its robustness. After image processing and data augmentation, using the combined dataset, and fine-tuning the base model, our proposed RetNet-10 model outperformed other automated methods for DR diagnosis.

MicroRNA Profiling from Tears as a Potential Non-invasive Method for Early Detection of Diabetic Retinopathy.

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

Diagnosis of Diabetic Retinopathy

Abstract: A diabetes-related eye condition called diabetic retinopathy (DR) can cause blindness or blurred vision. The risk of vision loss can be significantly reduced with early DR diagnosis and treatment. Due to the heterogeneity of retinal alterations between disease stages and the significant proportion of misdiagnosed and untreated DR cases, such diagnosis is a timeconsuming and challenging endeavour. A transfer learning method is used to speed up the process while several preprocessing algorithms are used to increase accuracy. The fundus image collection from the online Kaggle datasets was used in our experiment.

Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function.

Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models, such as convolutional neural networks (CNNs), are largely used in DR detection through the classification of blood vessel pixels from the remaining pixels. In this paper, an improved activation function was proposed for diagnosing DR from fundus images that automatically reduces loss and processing time. The DIARETDB0, DRIVE, CHASE, and Kaggle datasets were used to train and test the enhanced activation function in the different CNN models. The ResNet-152 model has the highest accuracy of 99.41% with the Kaggle dataset. This enhanced activation function is suitable for DR diagnosis from retinal fundus images.

Overview of global publications on machine learning in diabetic retinopathy from 2011 to 2021: Bibliometric analysis.

To comprehensively analyze and discuss the publications on machine learning (ML) in diabetic retinopathy (DR) following a bibliometric approach. The global publications on ML in DR from 2011 to 2021 were retrieved from the Web of Science Core Collection (WoSCC) database. We analyzed the publication and citation trend over time and identified highly-cited articles, prolific countries, institutions, journals and the most relevant research domains. VOSviewer and Wordcloud are used to visualize the mainstream research topics and evolution of subtopics in the form of co-occurrence maps of keywords. By analyzing a total of 1147 relevant publications, this study found a rapid increase in the number of annual publications, with an average growth rate of 42.68%. India and China were the most productive countries. IEEE Access was the most productive journal in this field. In addition, some notable common points were found in the highly-cited articles. The keywords analysis showed that "diabetic retinopathy", "classification", and "fundus images" were the most frequent keywords for the entire period, as automatic diagnosis of DR was always the mainstream topic in the relevant field. The evolution of keywords highlighted some breakthroughs, including "deep learning" and "optical coherence tomography", indicating the advance in technologies and changes in the research attention. As new research topics have emerged and evolved, studies are becoming increasingly diverse and extensive. Multiple modalities of medical data, new ML techniques and constantly optimized algorithms are the future trends in this multidisciplinary field.

Diabetic Retinopathy and Dementia Association, Beyond Diabetes Severity

To investigate whether associations between diabetic retinopathy (DR) and dementia and Alzheimer's disease (AD) remain significant after controlling for several measures of diabetes severity. Retrospective cohort study. Adult Changes in Thought (ACT) is a prospective cohort study of adults aged ≥65 years, randomly selected and recruited from the membership rolls of Kaiser Permanente Washington, who are dementia free at enrollment and followed biennially until incident dementia. The ACT participants were included in this study if they had type 2 diabetes mellitus at enrollment or developed it during follow-up, and data were collected through September, 2018 (3516 person-years of follow-up). Diabetes was defined by ≥ 2 diabetes medication fills in 1 year. Diagnosis of DR was based on International Classification of Diseases Ninth and Tenth Revision codes. Estimates of microalbuminuria, long-term glycemia, and renal function from longitudinal laboratory records were used as indicators of diabetes severity. Alzheimer's disease and dementia were diagnosed using research criteria at expert consensus meetings. A total of 536 participants (median baseline age 75 [interquartile range 71-80], 54% women) met inclusion criteria. Significant associations between DR >5 years duration with dementia (hazard ratio 1.81 [95% CI 1.23, 2.65]) and AD (1.80 [1.15, 2.82]) were not altered by adjustment for estimates of microalbuminuria, long-term glycemia, and renal function (dementia: 1.69 [1.14, 2.50]; AD: 1.73 [1.10, 2.74]). Among people with type 2 diabetes, DR itself appears to be an important biomarker of dementia risk in addition to glycemia and renal complications.

Do Statins Have a Protective Effect Against Neuro-Autoimmune Disease and Comorbidity?

Objective To investigate the neuroprotective potential of statins (ST) against neuro-autoimmune disease (NAD) in patients, and risks of associated comorbidities. Background ST have been shown to provide neuroprotective benefits in patients with cardiovascular disease and related risk factors. However, the extent of neuroprotective potential of ST medications in patients with NAD has not been determined. Design/Methods A retrospective analysis on 34464 patients hospitalized at a tertiary care center in a major metropolitan area was conducted. 7527 patients were in the ST group, while 26937 patients were not taking any statins (nST). 190 patients had a NAD diagnosis and 10818 had diabetes mellitus (DM). 702 patients carried a diagnosis of diabetic neuropathy and/or diabetic retinopathy (DMNR). NAD patients were further categorized as those with or without ST medications (aST and anST). The outcomes compared included the prevalence of NAD, comorbidities, encephalopathy, and overall neurological complications. Results The prevalence of NAD was 0.39% and 0.60% in ST and nST, respectively (p < 0.05). 69% of aST and 27% of anST had DM (p < 0.00001). Patients in aST had a significantly higher amount of DMNR than those in anST (13.8%, 0.62%, p < 0.0001) and high risk comorbidity (45%, 15%, p < 0.01). There was no significant difference between aST and anST in the prevalence of encephalopathy (10%, 6%, p > 0.05) and overall neurological complications (17%, 14%, p > 0.05). Conclusions These results suggest that patients taking ST may be associated with a lower risk of NAD with no increase in overall neurological complications during hospitalization. Despite these findings, ST may be associated with an increased burden of disease and higher prevalence of progressive neuropathy.

Values of optical coherence tomography angiography for diagnosing diabetic retinopathy and evaluating treatment outcomes

To compare the consistency between fundus fluorescein angiography (FFA) and optical coherence tomography angiography (OCTA) for the diagnosis of diabetic retinopathy (DR). Ninety-six diabetic patients (185 eyes) treated from January 2019 to December 2019 underwent OCTA and FFA. The image characteristics of fundus lesions were recorded. Sixty-nine patients (137 eyes) who were diagnosed with DR by both examinations and needed to receive panretinal photocoagulation (PRP) were selected. The retinal nerve fiber layer (RNFL) thickness, macular superficial vascular complex (SVC) and deep vascular complex (DVC) blood flow density, 300μm area surrounding foveal avascular zone (FAZ) (FD300) blood flow density and FAZ parameters were compared. The Kappa coefficient of FFA and OCTA for diagnosing DR was 0.537 (P=0.000). FFA and OCTA had substantial consistency for detecting retinal microaneurysms and macular edema (Kappa coefficient=0.643/0.616, P=0.000), perfect consistency for detecting retinal neovascularization and retinal non-perfusion area (Kappa coefficient=0.809/0.832, P=0.000), and moderate consistency for detecting structural changes in the macular ring (Kappa coefficient=0.423, P=0.000). The RNFL thickness in the peripapillary and the superior temporal, temporal inferior, inferior nasal and superior nasal regions rose 1 week after PRP but declined 1 year after treatment (P<0.05). The macular SVC, DVC and FD300 blood flow density declined 1 week after PRP but rose 1 year after treatment (P<0.05). OCTA shows consistency with FFA for diagnosing DR, which remedies the deficiency of FFA. The reduction in fundus lesions after PRP can be quantified by OCTA.

Reasons for the Late Presentation of Diabetic Retinopathy in Saudi Arabia: A Survey of Patients Who Presented with Advanced Proliferative Diabetic Retinopathy to a Tertiary Eye Hospital.

This study aimed to understand the barriers that contribute to diagnostic and therapeutic delays in patients with advanced proliferative diabetic retinopathy (PDR). This cross-sectional study targeted patients with advanced PDR who never received any ocular intervention at King Khaled Eye Specialist Hospital in Saudi Arabia. An Arabic-language questionnaire was used to interview the participants over a period of 6 months. The questionnaire comprised sociodemographic questions followed by several sections to assess the causes of delay in diagnosis and management. Variables were analyzed descriptively and reported as numbers and percentages using SPSS 22. A total of 338 patients were included in the study. Most patients were older than 50 years (60.4%), and decreased vision was the main complaint at presentation (81%). Vitreous hemorrhage was the most common diagnosis (46%). Patients' lack of knowledge about the importance of DR screening programs and problems with healthcare system screenings were the most frequent causes of delay in diabetic retinopathy (DR) diagnosis and management. DR is still a major cause of permanent blindness that is treatable with regular follow-up and timely management. Even though DR screening and treatment in Saudi Arabia have improved drastically over the last years, socioeconomic and health system factors remain barriers to the improvement of outcomes of DR.

Optic disc localization using graph traversal algorithm along blood vessel in polar retinal image

The optic disc (OD) is an important landmark of the retina and its location is essential for computer-aided diagnosis of diabetic retinopathy and glaucoma. This paper presented a new method that applies the proposed graph traversal algorithm for vertical vascular infrastructure to detect root nodes representing the location of optical discs. The proposed algorithms were designed to process the terrific view of blood vessels from a polar image without depending on the prominent OD characteristic of circular/elliptical shape and brightness. Before constructing the vascular infrastructure, the new multi-scale and multi-orientation top-hat filters were proposed to properly approximate blood vessels of different sizes and directions. The proposed method provided efficiency and certainty for complex retinal images such as low contrast OD with low illumination and distorted OD shape due to complex vascular structure. The algorithms evaluation was performed on five public fundus image databases for retinal image analysis. Experimental results revealed that the proposed algorithm identified valid optical discs with high accuracy as 91.21%, 95.09%, 88.45%, 91.66%, and 95.01% for the five corresponding data sets and outperformed comparison methods.

Diabetic retinopathy prediction using Progressive Ablation Feature Selection: A comprehensive classifier evaluation

Diabetic Retinopathy (DR) is the leading cause of blindness among adults in the U.S. Since DR is asymptomatic at the early stages, diabetic patients do not feel the uncomfortable ophthalmic exams necessary. However, existing DR diagnosis approaches mainly rely on fundus images that require access to ophthalmologists and special equipment, which are typically unavailable in rural areas. Machine-learning-based predictive models could help primary care physicians identify patients with a high risk of DR and confidently recommend ophthalmic exams. However, existing DR prediction models require a large number of independent variables, making them challenging to utilize in a clinical setting. In this study, we designed a novel “Progressive Ablation Feature Selection method with XGBoost” which successfully reduced the number of predictors from 25 to 9 to create a more user-friendly DR prediction model without sacrificing accuracy with an Area Under Curve of 96.61%. This study suggests that diabetic retinopathy is closely associated with creatinine, followed by neuropathy, hematocrit, BUN, nephropathy, albumin, race, calcium, and sodium. We provide an insight into each selected feature and its medical associations with DR. The result of this work will help physicians use a small set of available variables to identify high-risk diabetic patients prone to develop DR. Medical doctors thus can intervene at the proper time to prevent vision loss. • More than 2.3 million diabetics across over 10 million visits analyzed. • A novel progressive ablation feature selection method. • Comes with medical analysis of selected features. • Diabetic retinopathy is closely associated with creatinine, followed by neuropathy, hematocrit, BUN, nephropathy, albumin, race, calcium, sodium, and anion gap blood.

A Smart Mobile App for the Detection of Diabetic Retinopathy using Artificial Intelligence

Diabetic Retinopathy (DR) is the main cause of blindness for people who have diabetes in the world. This condition occur when high blood sugar levels cause damages to the blood vessel in the retina. These blood vessels can swell and leak thereby cause damage to the normal vision. In this system we are developing an Artificial intelligence-Based smart mobile application for the diagnosis and treatment of diabetic retinopathy. Using Tensor flow mathematical library, the app analyses the eye fund us image through deep learning from Kaggle database. The app would be useful in promoting self evaluation and timely treatment of Diabetic Retinopathy (DR) by physicians. The app would achieve an accuracy about approximately 80-90% and have an overall good performance

Predicting Diabetic Retinopathy Using Machine Learning

Over the last 3 decades, advances in laser surgery and intraocular drug delivery have decreased the risk of diabetic retinopathy. However, the use of artificial intelligence and other forms of data analysis in retinopathy screening has not had the same advances. This project aimed to analyze the National Health and Nutrition Examination Survey and to develop the best machine learning model to predict the disease. Violin plots were created to compare the distribution of diabetic retinopathy diagnosis with gender. The plot showed that females who had said they were not taking insulin were less likely to be diagnosed with diabetic retinopathy. Another violin plot showed that those with hypertension who were taking insulin were less likely to have diabetic retinopathy. Histograms were also created to show the distributions of the variables, color-coded by retinopathy diagnosis. Specifically, it was found that the bigger the time gaps since the diabetes diagnosis, the more likely a person suffers from diabetic retinopathy. Finally, multiple machine learning models were tested and these were the most accurate in predicting diabetic retinopathy with an 80% accuracy, LinearSVC, CalibratedClassifierCV, and Logistic Regression.