Detection Of Diabetic Retinopathy Research Articles

Enhancing Diabetic Retinopathy Detection Using Pixel Color Amplification and EfficientNetV2: A Novel Approach for Early Disease Identification

Enhancing Diabetic Retinopathy Detection Using Pixel Color Amplification and EfficientNetV2: A Novel Approach for Early Disease Identification

Prevalence of Diabetic Retinopathy among Self-reported Newly Diagnosed Diabetics

Introduction: Diabetic Retinopathy (DR) poses a significant threat to individuals with diabetes, leading to visual impairment and blindness if not promptly addressed. This study focuses on the prevalence of diabetic retinopathy among self-reported newly diagnosed diabetics. Material method: This is a prospective study on 200 self-reported newly diagnosed diabetics from April 2022 to May 2023. Patient data, including complaints, diabetes duration, family history, hypertension, and therapy details, were comprehensively analyzed. Complete Ocular examinations including best-corrected visual acuity, intraocular pressure, slit lamp examination, fundus examinations, and Fundus fluorescein angiography were done. Diabetic Retinopathy was graded using a modified ETDRS grading system. Laboratory investigations included complete blood count, fasting and postprandial blood sugar, lipid profile, renal function test, and Hb1AC levels, providing a thorough assessment of patients’ ocular and systemic health. Result: Out of the 200 diabetics, 43.5% had diabetic retinopathy, with a slightly higher prevalence in males (44.64%) compared to females. The age group 50 - 59 had the highest prevalence (74.19%), and Hindus, Muslims, and other communities showed comparable percentages. Family history and hypertension were significant factors, with 88% of those with a family history developing retinopathy. NPDR was predominant (87.35%), while 12.64% had PDR. Clinically Significant Macular Edema (CSME) was present in 59.77%, with 80.76% of these cases having concomitant hypertension. Conclusion: The study emphasizes the imperative for early detection and comprehensive management of diabetic retinopathy among newly diagnosed diabetics. Age, gender, family history, and hypertension emerge as crucial determinants, advocating for integrated care and targeted screening programs to safeguard vision.

Severity Detection of Diabetic Retinopathy Using Deep Learning Algorithms

Severity Detection of Diabetic Retinopathy Using Deep Learning Algorithms

Diabetic retinopathy detection using supervised and unsupervised deep learning: a review study

The severe progression of Diabetes Mellitus (DM) stands out as one of the most significant concerns for healthcare officials worldwide. Diabetic Retinopathy (DR) is a common complication associated with diabetes, particularly affecting individuals between the ages of 18 and 65. As per the findings of the International Diabetes Federation (IDF) report, 35–60% of individuals suffering from DR possess a diabetes history. DR emerges as a leading cause of worldwide visual impairment. Due to the absence of ophthalmologists worldwide, insufficient health resources, and healthcare services, patients cannot get timely eye screening services. Automated computer-aided detection of DR provides a wide range of potential benefits. In contrast to traditional observer-driven techniques, automatic detection allows for a more objective analysis of numerous images in a shorter time. Moreover, Unsupervised Learning (UL) holds a high potential for image classification in healthcare, particularly regarding explainability and interpretability. Many studies on the detection of DR with both supervised and unsupervised Deep Learning (DL) methodologies are available. Surprisingly, none of the reviews presented thus far have highlighted the potential benefits of both supervised and unsupervised DL methods in Medical Imaging for the detection of DR. After a rigorous selection process, 103 articles were retrieved from four diverse and well-known databases (Web of Science, Scopus, ScienceDirect, and IEEE). This review provides a comprehensive summary of both supervised and unsupervised DL methods applied in DR detection, explaining the significant benefits of both techniques and covering aspects such as datasets, pre-processing, segmentation techniques, and supervised and unsupervised DL methods for detection. The insights from this review will aid academics and researchers in medical imaging to make informed decisions and choose the best practices for DR detection.

Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy

ObjectiveVision Transformers (ViTs) have shown promising performance in various classification tasks previously dominated by Convolutional Neural Networks (CNNs). However, the performance of ViTs in referable Diabetic Retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR. DesignRetrospective study ParticipantsA total of 48,269 retinal images from the open-source Kaggle DR detection dataset, the Messidor-1 dataset and the Singapore Epidemiology of Eye Diseases (SEED) study were included. MethodUsing 41,614 retinal photographs from the Kaggle dataset, we developed five CNN (VGG19, ResNet50, InceptionV3, DenseNet201 and EfficientNetV2S) and four ViTs models (VAN_small, CrossViT_small, ViT_small and SWIN_tiny) for the detection of referable DR. We defined the presence of referable DR as eyes with moderate or worse DR. The comparative performance of all nine models was evaluated in the Kaggle internal test dataset (with 1,045 study eyes), and in two external test sets, the SEED study (5,455 study eyes) and the Messidor-1 (1,200 study eyes). Main Outcome MeasuresArea Under Receiver Operative Curve (AUC), Specificity and Sensitvity. ResultsAmong all models, the SWIN transformer displayed the highest area under the curve (AUC) of 95.7% on the internal test set, significantly outperforming the CNN models (all P<0.001). The same observation was confirmed in the external test sets, with the SWIN transformer achieving AUC of 97.3% in SEED and 96.3% in Messidor-1. When specificity level was fixed at 80% for the internal test, the SWIN transformer achieved the highest sensitivity of 94.4%, significantly better than all the CNN models (sensitivity levels ranging between 76.3% and 83.8%; all P <0.001). This trend was also consistently observed in both external test sets. ConclusionOur findings demonstrate that ViTs provide superior performance over CNNs in detecting referable DR from retinal photographs. These results point to the potential of utilizing ViT models to improve and optimize retinal photo-based deep learning for referable DR detection.

Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning

Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98 % accuracy which is superior to previous methods.

Cost-Effectiveness of Diabetic Retinopathy Screening for Newly Diagnosed Type 2 Diabetic Patients: A Nationwide Population-based Propensity Score-Matched Cohort Study

AimsThis study investigated the association between the frequency of screening for diabetic retinopathy (DR) versus the development of DR and corresponding medical expenses among patients newly diagnosed with type 2 diabetes mellitus (T2DM). MethodsThis longitudinal, population-based study used the Taiwan National Health Insurance Research Database (2004 to 2020) as a data source. Propensity score matching (PSM) (sex, age, comorbidities and concurrent medication use) was employed in the grouping of T2DM patients according to different frequency of DR screening. Outcome measures included the proportion of patients who developed DR, who received DR treatment, and the associated medical expenses and hospitalizations. ResultsThe 17-year cohort included 337,046 patients. After PSM, three groups each containing 35,739 patients were assembled and analyzed. Compared to low-frequency screening, high-frequency screening was more effective in detecting patients requiring treatment; however, the net cost for treatment was significantly lower. Standard-frequency screening appears to provide the best balance in terms of DR detection, diagnosis interval, the risk of DR-related hospitalization, and DR treatment costs. ConclusionsIn this real-world cohort study covering all levels of the healthcare system, infrequent screening was associated with delayed diagnosis and elevated treatment costs, while a fundus screening interval of 1-2 years proved optimal in terms of detection and medical expenditures.

Diabetic Retinopathy Detection Using Deep Learning with Optimized Feature Selection

Diabetic Retinopathy Detection Using Deep Learning with Optimized Feature Selection

Quantum driven deep learning for enhanced diabetic retinopathy detection

Traditional convolutional neural networks (CNNs) have shown potential for recognizing retinopathy caused by diabetes (DR). However, developing quantum computing has the possibility for improved feature representation. We propose a hybrid approach that combines classical CNNs with quantum circuits to capitalize on both classical and quantum information for DR classification. Using the Keras and Qiskit frameworks, our model encodes picture features into quantum states, allowing for richer representations. Through experiments on a collection of retinal pictures, our model displays competitive performance, with excellent reliability and precision in categorizing DR severity levels. This combination of classical and quantum paradigms offers a fresh approach to enhancing DR diagnosis and therapy.

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

PSO-HRVSO: Segmentation of Retinal Vessels Through Homomorphic Filtering Enhanced by PSO Optimization

The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature. The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage. The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively. The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

Diabetic retinopathy detection with fundus images based on deep model enabled chronological rat swarm optimization

Diabetic retinopathy detection with fundus images based on deep model enabled chronological rat swarm optimization

Detecting Diabetic Retinopathy using Mobile Net

Diabetic retinopathy (DR) is a prevalent complication of diabetes mellitus that can lead to This paper presents a novel machine learning approach using TensorFlow for the automated detection and stage classification of diabetic retinopathy from retinal images. The study utilizes a comprehensive dataset of retinal images, which undergo preprocessing steps to enhance image quality and prepare them for analysis. A deep learning model based on convolutional neural networks (CNNs) is developed using TensorFlow, leveraging its efficient computational capabilities and optimization tools. The proposed model using Mobile Net is trained and validated on the retinal image dataset to classify diabetic retinopathy into different stages, ranging from mild to severe. Keywords— Deep Learning, Mobile Net, Diabetic Retinopathy (DR) , Dataset

Reliability of Clinical Grading of Diabetic Retinopathy Compared to Grading of Ultra-widefield Images

Purpose: Evaluate the reliability of clinical grading of diabetic retinopathy (DR) severity compared to grading on ultra-widefield pseudocolor fundus (UWF-CF) and ultra-widefield fluorescein angiography (UWF-FA) images and their relative detection of sight-threatening DR (STDR) and referable DR. Methods: We analyzed 184 diabetic eyes. UWF-CF and UWF-FA images were graded based on the International Clinical Diabetic Retinopathy (ICDR) severity scale. Agreement between clinical and UWF-based severity grading was evaluated using Cohen’s kappa coefficient. The rate of STDR and referable DR was evaluated for each grading method. Results: We found moderate agreement between clinical grading and UWF-CF (k=.456, p &lt;.001) and between UWF-CF and UWF-FA (k= .443, p &lt;.001). Agreement between clinical grading and UWF-FA was fair (k=.397, p &lt;.001). UWF-based grading identified a higher DR grade in 56 eyes (30%) on UWF-CF and 85 eyes (46.2%) on UWF-FA. Compared to clinical grading, UWF-FA detected a higher rate of STDR (44%; 81/184 vs 22.3%; 41/184), while UWF-CF detected more referable eyes (58.1%;107/184 vs 45.65%;84/184). Conclusion: UWF-CF is a valuable tool for identifying referable eyes and can be a useful, non-invasive adjunct to clinical grading. Our results suggest that UWF-FA is particularly useful for detecting unsuspected STDR in eyes with clinically referable DR.

Three-year follow-up study investigating changes in retinal vascular morphology and the relationship with diabetic retinopathy in children with type 1 diabetes mellitus.

This study aimed to investigate changes in retinal microvascular morphology and associated factors, and their relationship with diabetic retinopathy (DR) in children with type 1 diabetes mellitus (T1DM). Thirty-eight children enrolled in this three-year follow-up study underwent complete ophthalmic examinations including fundus photography. Retinal vascular parameters were measured automatically and compared between baseline and follow-up. Multiple linear regression was used to investigate factors affecting changes in vascular parameters. Binary logistic regression was used to analyze the relationship between retinal microvascular morphology and DR. The caliber of all retinal vessels (within 1-1.5 papillary diameter [PD] from the center of the optic disc, P = 0.030; 1.5-2 PD, P = 0.003), arterioles, and venules (1.5-2 PD, P = 0.001) were narrower in nearly all regions in the follow-up group compared with the baseline group. Vascular tortuosity increased in the central part of the retina and decreased in the periphery. The density (1-1.5 PD, P = 0.030) and fractal dimension (P = 0.037) of retinal vessels were increased at the end of the follow-up compared with baseline. Retinal vascular caliber was independently correlated with DR (odds ratio 0.793 [95% confidence interval 0.633-0.993]; P = 0.044). Retinal microvascular morphology in children with T1DM varied with the disease course. Narrower retinal vessels may be an independent risk factor for DR. Results of this study emphasized the importance of regular follow-up of fundus vascular morphology for the detection of early DR in children with T1DM.

Diabetic Retinopathy Stage Detection Using CNN and Inception V3

The project explores the deployment of Convolutional Neural Networks (CNN) and the Inception V3 model for the automated detection and classification of diabetic retinopathy stages using fundus images. Recognizing diabetic retinopathy as a leading cause of blindness among the working-age population globally, this research aims to streamline the diagnostic process, traditionally reliant on the manual examination by ophthalmologists. Through the utilization of the DRIVE and STARE datasets, the project benchmarks the performance of CNN and Inception V3 models in accurately categorizing the severity of diabetic retinopathy into five distinct stages. The comparison between these models is grounded on parameters such as accuracy, loss, and predicted value, with findings indicating Inception V3's superiority in both performance metrics and diagnostic precision. This advancement could significantly contribute to early and more accessible detection of diabetic retinopathy, thereby mitigating progression towards blindness. Furthermore, the project underscores the potential of deep learning algorithms in enhancing diagnostic methodologies for retinal diseases, paving the way for future explorations in the field of medical imaging and artificial intelligence.

Correction: Srinivasan et al. Detection and Grade Classification of Diabetic Retinopathy and Adult Vitelliform Macular Dystrophy Based on Ophthalmoscopy Images. Electronics 2023, 12, 862

There was an error in the original publication [...]

Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy.

Purpose: To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Methods: Two-hundred one patients (mean age 65 ± 13 years) with type 1 diabetes mellitus or type 2 diabetes mellitus were included. All patients were undergoing a retinography and spectral domain optical coherence tomography (SD-OCT, DRI 3D OCT-2000, Topcon) of the macula. The retinal photographs were graded using two validated AI DR screening software (Eye Art TM and IDx-DR) designed to identify more than mild DR. Results: Retinal images of 201 patients were graded. DR (more than mild DR) was detected by the ophthalmologists in 38 (18.9%) patients and by the AI-algorithms in 36 patients (with 30 eyes diagnosed by both algorithms). Ungradable patients by the AI software were 13 (6.5%) and 16 (8%) for the Eye Art and IDx-DR, respectively. Both AI software strategies showed a high sensitivity and specificity for detecting any more than mild DR without showing any statistically significant difference between them. Conclusions: The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.

OPTIVISION: ENHANCING DIABETIC RETINOPATHY DETECTION USING DEEP LEARNING

The diabetic retinopathy is an eye disease associated with chronic diabetes. It is the leading cause of blindness in people. Diabetic Retinopathy is complication of diabetes that targets the eyes by damaging the retinal blood vessels. Primarily occurs when the blood sugar level is unmanageable. Therefore the person with diabetes mellitus is always at a high risk of acquiring this disease. The present work considers a deep learning methodology specifically a convolution neural network which is applied for the early detection of diabetic retinopathy is detected on time. It classifies the fundus images based n its severity levels as N0 DR, and Yes DR. The main objective of this work is t build a stable and noise compatible system for detection of diabetic retinopathy. This work employs the deep learning methodology for detecting the diabetic retinopathy based on severity level. Many processes were carried out before feeding the images to the network. Key Words: Diabetic retinopathy detection, Deep learning, CNN, Real time classification. The diabetic retinopathy is an eye disease associated with chronic diabetes. It is the leading cause of blindness in people. Diabetic Retinopathy is complication of diabetes that targets the eyes by damaging the retinal blood vessels. Primarily occurs when the blood sugar level is unmanageable. Therefore the person with diabetes mellitus is always at a high risk of acquiring this disease. The present work considers a deep learning methodology specifically a convolution neural network which is applied for the early detection of diabetic retinopathy is detected on time. It classifies the fundus images based n its severity levels as N0 DR, and Yes DR. The main objective of this work is t build a stable and noise compatible system for detection of diabetic retinopathy. This work employs the deep learning methodology for detecting the diabetic retinopathy based on severity level. Many processes were carried out before feeding the images to the network. Key Words: Diabetic retinopathy detection, Deep learning, CNN, Real time classification.

Vision Saver Deep Learning Approaches for Diabetic Retinopathy Detection

One of the main causes of adult blindness and a frequent consequence of diabetes is diabetic retinopathy. In order to preserve eyesight, diabetic retinopathy must be identified and treated early. In this study, "Vision Saver," a deep learning-based method for the automated detection and treatment of diabetic retinopathy, is introduced. Vision Saver provides a comprehensive solution for the detection of retinal problems by utilizing deep neural networks, enabling prompt patient care and intervention. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two cutting-edge deep learning approaches, are used by the proposed system to evaluate retinal images and provide precise diagnoses. Additionally, we investigate how generative adversarial networks (GANs) might improve image quality and produce artificial data for training. The architecture of Vision Saver is built to handle both fundus photos and optical coherence tomography (OCT) images, enabling a flexible method of evaluating diabetic retinopathy. Vision Saver's management component includes not only detection but also monitoring of therapy progression. The system aids medical personnel in making educated judgments about interventions like laser therapy, injections, or surgical operations through ongoing monitoring and follow-up. The development and assessment of Vision Saver are covered in this essay, with a focus on how well it performs in terms of sensitivity, specificity, and accuracy. We also discuss concerns with model interpretability, data protection, and integration into clinical workflows. Ultimately, the objective is to improve patient outcomes and lessen the burden of blindness brought on by diabetic retinopathy by offering a comprehensive solution that improves screening and management of the illness. Deep learning in healthcare has a lot of potential, and Vision Saver is an example of how it can significantly improve the lives of people with diabetic retinopathy.