Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning

Diabetic retinopathy (DR) remains a leading cause of vision impairment and blindness among individuals with diabetes, necessitating innovative approaches to screening and management. This editorial explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing DR care. AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy, efficiency, and accessibility of DR screening, helping to overcome barriers to early detection. These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision, enabling clinicians to make more informed decisions. Furthermore, AI-driven solutions hold promise in personalizing management strategies for DR, incorporating predictive analytics to tailor interventions and optimize treatment pathways. By automating routine tasks, AI can reduce the burden on healthcare providers, allowing for a more focused allocation of resources towards complex patient care. This review aims to evaluate the current advancements and applications of AI and ML in DR screening, and to discuss the potential of these technologies in developing personalized management strategies, ultimately aiming to improve patient outcomes and reduce the global burden of DR. The integration of AI and ML in DR care represents a paradigm shift, offering a glimpse into the future of ophthalmic healthcare.

Boosting medical diagnostics with a novel gradient-based sample selection method

In the rapidly expanding landscape of medical data, the need for innovative approaches to maximize classification performance has become increasingly critical. As data volumes grow, ensuring that diagnostic systems work with accurate and relevant data is paramount for effective and generalizable classification. This study introduces a novel gradient-based sample selection method, the first of its kind in the literature, specifically designed to enhance classification accuracy by removing redundant and non-informative data. Unlike traditional methods that focus solely on feature selection, this approach integrates an advanced sample selection technique to optimize the input data, leading to more accurate and efficient diagnostics. The method is validated on multiple disease datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset and the Cleveland Coronary Artery Disease (CAD) dataset, demonstrating its broad applicability and effectiveness. To address dataset imbalance, the Adaptive Synthetic Sampling (ADASYN) method is employed, followed by Particle Swarm Optimization (PSO) for feature selection. The refined datasets are then classified using a Support Vector Machine (SVM), showing that even traditional classifiers can achieve substantial improvements when enhanced with advanced sample selection. The results underscore the critical importance of precise sample selection in boosting classification performance, setting a new standard for computer-aided diagnostics and paving the way for future innovations in handling large and complex medical datasets.

Total variation regularized tensor ring decomposition for OCT image denoising and super-resolution

This paper suggests a novel hybrid tensor-ring (TR) decomposition and first-order tensor-based total variation (FOTTV) model, known as the TRFOTTV model, for super-resolution and noise suppression of optical coherence tomography (OCT) images. OCT imaging faces two fundamental problems undermining correct OCT-based diagnosis: significant noise levels and low sampling rates to speed up the capturing process. Inspired by the effectiveness of TR decomposition in analyzing complicated data structures, we suggest the TRFOTTV model for noise suppression and super-resolution of OCT images. Initially, we extract the nonlocal 3D patches from OCT data and group them to create a third-order low-rank tensor. Subsequently, using TR decomposition, we extract the correlations among all modes of the grouped OCT tensor. Finally, FOTTV is integrated into the TR model to enhance spatial smoothness in OCT images and conserve layer structures more effectively. The proximal alternating minimization and alternative direction method of multipliers are applied to solve the obtained optimization problem. The effectiveness of the suggested method is verified by four OCT datasets, demonstrating superior visual and numerical outcomes compared to state-of-the-art procedures.

Fine-Grained Self-Supervised Learning with Jigsaw puzzles for medical image classification

Classifying fine-grained lesions is challenging due to minor and subtle differences in medical images. This is because learning features of fine-grained lesions with highly minor differences is very difficult in training deep neural networks. Therefore, in this paper, we introduce Fine-Grained Self-Supervised Learning(FG-SSL) method for classifying subtle lesions in medical images. The proposed method progressively learns the model through hierarchical block such that the cross-correlation between the fine-grained Jigsaw puzzle and regularized original images is close to the identity matrix. We also apply hierarchical block for progressive fine-grained learning, which extracts different information in each step, to supervised learning for discovering subtle differences. Our method does not require an asymmetric model, nor does a negative sampling strategy, and is not sensitive to batch size. We evaluate the proposed fine-grained self-supervised learning method on comprehensive experiments using various medical image recognition datasets. In our experiments, the proposed method performs favorably compared to existing state-of-the-art approaches on the widely-used ISIC2018, APTOS2019, and ISIC2017 datasets.

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.

The Economic Burden of Diabetic Retinopathy in Jordan: Cost Analysis and Associated Factors

Objective

Diabetic retinopathy (DR) is the leading cause of visual loss worldwide in patients with diabetes mellitus (DM). The aims of our study are to describe the costs associated with (DR) and to evaluate its economic impact in Jordan.

Methods

Retrospectively, we included all patients with DM and classified them according to the severity of DR. Data regarding medical history, ophthalmic history, stage of DR, presence of DME, and the ophthalmic procedures and operations were collected. The total DR-related cost was measured as a direct medical cost for the outpatient and inpatient services.

Results

Two hundred and twenty-nine patients were included in the study. Only 49.7% of the patients presented without DR, and 21% presented with diabetic macular edema (DME) unilaterally or bilaterally. The DR-related cost was significantly associated with insulin-based regimens, longer duration of DM, higher HbA1c levels, worse stage of DR at presentation, the presence of DME at presentation, the presence of glaucoma, and increased mean number of intravitreal injections, laser sessions, and surgical operations. Multivariate analysis should the presenting stage of DR, presence of DME, and the presence of DME be the independent factors affecting the DR-related cost.

Conclusion

This study is the first study to be conducted in Jordan and encourages us to establish a screening program for DR for earlier detection and treatment. DM control and treatment compliance will reduce the heavy costs of the already exhausted healthcare and financial system.

Correlation study of renal function indices with diabetic peripheral neuropathy and diabetic retinopathy in T2DM patients with normal renal function

Background

The anticipation of diabetes-related complications remains a challenge for numerous T2DM patients, as there is presently no effective method for early prediction of these complications. This study aims to investigate the association between renal function-related indicators and the occurrence of peripheral neuropathy and retinopathy in individuals diagnosed with type 2 diabetes mellitus (T2DM) who currently have normal renal function.

Methods

Patients with T2DM who met the criteria were selected from the MMC database and divided into diabetic peripheral neuropathy (DPN) and diabetic retinopathy (DR) groups, with a total of 859 and 487 patients included, respectively. Multivariate logistic regression was used to analyze the relationship between blood urea nitrogen (BUN), creatinine (Cr), uric acid (UA), urine albumin(ALB), albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), and diabetic peripheral neuropathy and retinopathy. Spearman correlation analysis was used to determine the correlation between these indicators and peripheral neuropathy and retinopathy in diabetes.

Results

In a total of 221 patients diagnosed with DPN, we found positive correlation between the prevalence of DPN and eGFR (18.2, 23.3, 35.7%, p < 0.05). Specifically, as BUN (T1: references; T2:OR:0.598, 95%CI: 0.403, 0.886; T3:OR:1.017, 95%CI: 0.702, 1.473; p < 0.05) and eGFR (T1: references; T2:OR:1.294, 95%CI: 0.857, 1.953; T3:OR:2.142, 95%CI: 1.425, 3.222; p < 0.05) increased, the odds ratio of DPN also increased. Conversely, with an increase in Cr(T1: references; T2:OR:0.86, 95%CI: 0.56, 1.33; T3:OR:0.57, 95%CI: 0.36, 0.91; p < 0.05), the odds ratio of DPN decreased. Furthermore, when considering sensitivity and specificity, eGFR exhibited a sensitivity of 65.2% and specificity of 54.4%, with a 95% confidence interval of 0.568-0.656.

Conclusion

In this experimental sample, we found a clear positive correlation between eGFR and DPN prevalence.

A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: an ensemble learning approach

Diabetes retinopathy (DR) is one of the leading causes of blindness globally. Early detection of this condition is essential for preventing patients' loss of eyesight caused by diabetes mellitus being untreated for an extended period. This paper proposes the design of an augmented bioinspired multidomain feature extraction and selection model for diabetic retinopathy severity estimation using an ensemble learning process. The proposed approach initiates by identifying DR severity levels from retinal images that segment the optical disc, macula, blood vessels, exudates, and hemorrhages using an adaptive thresholding process. Once the images are segmented, multidomain features are extracted from the retinal images, including frequency, entropy, cosine, gabor, and wavelet components. These data were fed into a novel Modified Moth Flame Optimization-based feature selection method that assisted in optimal feature selection. Finally, an ensemble model using various ML (machine learning) algorithms, which included Naive Bayes, K-Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forests, and Logistic Regression were used to identify the various severity complications of DR. The experiments on different openly accessible data sources have shown that the proposed method outperformed conventional methods and achieved an Accuracy of 96.5% in identifying DR severity levels.

Hybrid Fusion of High-Resolution and Ultra-Widefield OCTA Acquisitions for the Automatic Diagnosis of Diabetic Retinopathy

Optical coherence tomography angiography (OCTA) can deliver enhanced diagnosis for diabetic retinopathy (DR). This study evaluated a deep learning (DL) algorithm for automatic DR severity assessment using high-resolution and ultra-widefield (UWF) OCTA. Diabetic patients were examined with 6×6 mm2 high-resolution OCTA and 15×15 mm2 UWF-OCTA using PLEX®Elite 9000. A novel DL algorithm was trained for automatic DR severity inference using both OCTA acquisitions. The algorithm employed a unique hybrid fusion framework, integrating structural and flow information from both acquisitions. It was trained on data from 875 eyes of 444 patients. Tested on 53 patients (97 eyes), the algorithm achieved a good area under the receiver operating characteristic curve (AUC) for detecting DR (0.8868), moderate non-proliferative DR (0.8276), severe non-proliferative DR (0.8376), and proliferative/treated DR (0.9070). These results significantly outperformed detection with the 6×6 mm2 (AUC = 0.8462, 0.7793, 0.7889, and 0.8104, respectively) or 15×15 mm2 (AUC = 0.8251, 0.7745, 0.7967, and 0.8786, respectively) acquisitions alone. Thus, combining high-resolution and UWF-OCTA acquisitions holds the potential for improved early and late-stage DR detection, offering a foundation for enhancing DR management and a clear path for future works involving expanded datasets and integrating additional imaging modalities.

Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning

If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be validated before choosing the best treatment. In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based feature extraction. Interconnected layers helps to identify the critical features or characteristics; in addition, Ant Colony systems also help choose the characteristics. Passing these chosen attributes through SVM with multiple kernels yielded the final classification model with promising accuracy. The experiment based on 750 features proves that the proposed approach has achieved an accuracy of 93%.

Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices

Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and classifying diabetic retinopathy on fundus retina images, numerous artificial intelligence-based algorithms have been put forth by the scientific community. Due to its real-time relevance to everyone's lives, smart healthcare is attracting a lot of interest. With the convergence of IoT, this attention has increased. The leading cause of blindness among persons in their working years is diabetic eye disease. Millions of people live in the most populous Asian nations, including China and India, and the number of diabetics among them is on the rise. To provide medical screening and diagnosis for this rising population of diabetes patients, skilled clinicians faced significant challenges. Our objective is to use deep learning techniques to automatically detect blind spots in eyes and determine how serious they may be. We suggest an enhanced convolutional neural network (ECNN) utilizing a genetic algorithm in this paper. The ECNN technique's accuracy results are compared to those of existing approaches like the K-nearest neighbor approach, convolutional neural network, and support vector machine with the genetic algorithm.

Oxidative Stress Implication in Retinal Diseases-A Review

Oxidative stress (OS) refers to an imbalance between free radicals (FRs), namely highly reactive molecules normally generated in our body by several pathways, and intrinsic antioxidant capacity. When FR levels overwhelm intrinsic antioxidant defenses, OS occurs, inducing a series of downstream chemical reactions. Both reactive oxygen species (ROS) and reactive nitrogen species (RNS) are produced by numerous chemical reactions that take place in tissues and organs and are then eliminated by antioxidant molecules. In particular, the scientific literature focuses more on ROS participation in the pathogenesis of diseases than on the role played by RNS. By its very nature, the eye is highly exposed to ultraviolet radiation (UVR), which is directly responsible for increased OS. In this review, we aimed to focus on the retinal damage caused by ROS/RNS and the related retinal pathologies. A deeper understanding of the role of oxidative and nitrosative stress in retinal damage is needed in order to develop targeted therapeutic interventions to slow these pathologies.

Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy

Deep learning evolves into a new form of machine learning technology that is classified under artificial intelligence (AI), which has substantial potential for large-scale healthcare screening and may allow the determination of the most appropriate specific treatment for individual patients. Recent developments in diagnostic technologies facilitated studies on retinal conditions and ocular disease in metabolism and endocrinology. Globally, diabetic retinopathy (DR) is regarded as a major cause of vision loss. Deep learning systems are effective and accurate in the detection of DR from digital fundus photographs or optical coherence tomography. Thus, using AI techniques, systems with high accuracy and efficiency can be developed for diagnosing and screening DR at an early stage and without the resources that are only accessible in special clinics. Deep learning enables early diagnosis with high specificity and sensitivity, which makes decisions based on minimally handcrafted features paving the way for personalized DR progression real-time monitoring and in-time ophthalmic or endocrine therapies. This review will discuss cutting-edge AI algorithms, the automated detecting systems of DR stage grading and feature segmentation, the prediction of DR outcomes and therapeutics, and the ophthalmic indications of other systemic diseases revealed by AI.