Retinal Image Analysis Research Articles

A Method for Retina Segmentation by Means of U-Net Network

Retinal image segmentation plays a critical role in diagnosing and monitoring ophthalmic diseases such as diabetic retinopathy and age-related macular degeneration. We propose a deep learning-based approach utilizing the U-Net network for the accurate and efficient segmentation of retinal images. U-Net, a convolutional neural network widely used for its performance in medical image segmentation, is employed to segment key retinal structures, including the optic disc and blood vessels. We evaluate the proposed model on a publicly available retinal image dataset, demonstrating interesting performance in automatic retina segmentation, thus showing the effectiveness of the proposed method. Our proposal provides a promising method for automated retinal image analysis, aiding in early disease detection and personalized treatment planning.

Early detection of age-related macular degeneration using Vision Transformer-based Architectures – A comparative study with offline metrics and data augmenting

Age-related macular degeneration (AMD) is one of the leading causes of vision loss in elderly adults around the world and is among the main visual impairments in Mexico. The difficulty of diagnosing AMD in its early stages motivates the use of advanced deep-learning methods that offer significant potential to improve diagnostic accuracy in retinal image analysis. In recent years, Transformer architectures for computer vision, such as Vision Transformer (ViT), Swin Transformer and BERT Pre-training of Image Transformers (BEiT) have provided a novel perspective for image analysis. This study presents a comparative analysis of these architectures, applied to AMD detection, focusing on each model's capability to classify the early stages of the disease. Although the small size of medical image datasets represented a challenge, our results suggest that ViT-based architectures and their derivatives achieve significant performance in AMD detection. BEiT is particularly notable for its consistently superior performance.

Advancing retinal image analysis: from preprocessing to lesion identification in diabetic retinopathy

Advancing retinal image analysis: from preprocessing to lesion identification in diabetic retinopathy

APPLICATIONS OF MULTIMODAL GENERATIVE ARTIFICIAL INTELLIGENCE IN A REAL-WORLD RETINA CLINIC SETTING.

This study evaluates a large language model, Generative Pre-trained Transformer 4 with vision, for diagnosing vitreoretinal diseases in real-world ophthalmology settings. A retrospective cross-sectional study at Bascom Palmer Eye Clinic, analyzing patient data from January 2010 to March 2023, assesses Generative Pre-trained Transformer 4 with vision's performance on retinal image analysis and International Classification of Diseases 10th revision coding across 2 patient groups: simpler cases (Group A) and complex cases (Group B) requiring more in-depth analysis. Diagnostic accuracy was assessed through open-ended questions and multiple-choice questions independently verified by three retina specialists. In 256 eyes from 143 patients, Generative Pre-trained Transformer 4-V demonstrated a 13.7% accuracy for open-ended questions and 31.3% for multiple-choice questions, with International Classification of Diseases 10th revision code accuracies at 5.5% and 31.3%, respectively. Accurately diagnosed posterior vitreous detachment, nonexudative age-related macular degeneration, and retinal detachment. International Classification of Diseases 10th revision coding was most accurate for nonexudative age-related macular degeneration, central retinal vein occlusion, and macular hole in OEQs, and for posterior vitreous detachment, nonexudative age-related macular degeneration, and retinal detachment in multiple-choice questions. No significant difference in diagnostic or coding accuracy was found in Groups A and B. Generative Pre-trained Transformer 4 with vision has potential in clinical care and record keeping, particularly with standardized questions. Its effectiveness in open-ended scenarios is limited, indicating a significant limitation in providing complex medical advice.

A wavelet CNN with appropriate feed-allocation and PSO optimized activations for diabetic retinopathy grading

This work modifies the architecture of conventional CNN with the integration of Multi-resolution Analysis (MRA) in a CNN framework for Diabetic Retinopathy (DR) diagnosis and grading. Here, the HF sub-bands are subjected to optimized activations and are directly fed to the fully connected layers, as it encompasses edge features. Unlike FD-Relu, the proposed function preserves significant negative coefficients, compared to the S-Relu, the proposed third-order S-Relu is optimized such that it sustains the activations in the range suitable for the wavelet coefficients. The coefficients of higher-order terms of the proposed 3rd-order S-Relu are optimized with PSO, fitting the maximum energy of the wavelet sub-bands to ensure High Frequency (HF) edge preservation. The authors re-architecture 3 different CNNs published in the Retinal Image analysis field, with spatial and wavelet inputs with optimized activations. The highest accuracy of 96% is attained with the AlexNet re-architecture, with 35,126 fundus images secured from the Kaggle dataset. As we can infer the proposed re-architecture wavelet CNN outperformed the multiscale shallow CNNs, multiscale attention net, and stacked CNNs with a 6.6, 0.3, 0.7 per cent increase in accuracy. The entire implementation of the wavelet CNN is made available under source code.

RFMiD: Retinal Image Analysis for multi-Disease Detection challenge

In the last decades, many publicly available large fundus image datasets have been collected for diabetic retinopathy, glaucoma, and age-related macular degeneration, and a few other frequent pathologies. These publicly available datasets were used to develop a computer-aided disease diagnosis system by training deep learning models to detect these frequent pathologies. One challenge limiting the adoption of a such system by the ophthalmologist is, computer-aided disease diagnosis system ignores sight-threatening rare pathologies such as central retinal artery occlusion or anterior ischemic optic neuropathy and others that ophthalmologists currently detect. Aiming to advance the state-of-the-art in automatic ocular disease classification of frequent diseases along with the rare pathologies, a grand challenge on “Retinal Image Analysis for multi-Disease Detection” was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2021). This paper, reports the challenge organization, dataset, top-performing participants solutions, evaluation measures, and results based on a new “Retinal Fundus Multi-disease Image Dataset” (RFMiD). There were two principal sub-challenges: disease screening (i.e. presence versus absence of pathology — a binary classification problem) and disease/pathology classification (a 28-class multi-label classification problem). It received a positive response from the scientific community with 74 submissions by individuals/teams that effectively entered in this challenge. The top-performing methodologies utilized a blend of data-preprocessing, data augmentation, pre-trained model, and model ensembling. This multi-disease (frequent and rare pathologies) detection will enable the development of generalizable models for screening the retina, unlike the previous efforts that focused on the detection of specific diseases.

Revolutionizing diabetic eye disease detection: retinal image analysis with cutting-edge deep learning techniques

Globally, glaucoma is a leading cause of visual impairment and vision loss, emphasizing the critical need for early diagnosis and intervention. This research explores the application of deep learning for automated glaucoma diagnosis using retinal fundus photographs. We introduce a novel cross-sectional optic nerve head (ONH) feature derived from optical coherence tomography (OCT) images to enhance existing diagnostic procedures. Our approach leverages deep learning to automatically detect key optic disc characteristics, eliminating the need for manual feature engineering. The deep learning classifier then categorizes images as normal or abnormal, streamlining the diagnostic process. Deep learning techniques have proven effective in classifying and segmenting retinal fundus images, enabling the analysis of a growing number of images. This study introduces a novel mixed loss function that combines the strengths of focal loss and correntropy loss to handle complex biomedical data with class imbalance and outliers, particularly in OCT images. We further refine a multi-task deep learning model that capitalizes on similarities across major eye-fundus activities and metrics for glaucoma detection. The model is rigorously evaluated on a real-world ophthalmic dataset, achieving impressive accuracy, specificity, and sensitivity of 100%, 99.8%, and 99.2%, respectively, surpassing state-of-the-art methods. These promising results underscore the potential of our deep learning algorithm for automated glaucoma diagnosis, with significant implications for clinical applications. By simultaneously addressing segmentation and classification challenges, our approach demonstrates its effectiveness in accurately identifying ocular diseases, paving the way for improved glaucoma diagnosis and early intervention.

Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic Diseases.

Cardiovascular diseases (CVDs) are a major cause of mortality globally, emphasizing the need for early detection and effective risk assessment to improve patient outcomes. Advances in oculomics, which utilize the relationship between retinal microvascular changes and systemic vascular health, offer a promising non-invasive approach to assessing CVD risk. Retinal fundus imaging and optical coherence tomography/angiography (OCT/OCTA) provides critical information for early diagnosis, with retinal vascular parameters such as vessel caliber, tortuosity, and branching patterns identified as key biomarkers. Given the large volume of data generated during routine eye exams, there is a growing need for automated tools to aid in diagnosis and risk prediction. The study demonstrates that AI-driven analysis of retinal images can accurately predict cardiovascular risk factors, cardiovascular events, and metabolic diseases, surpassing traditional diagnostic methods in some cases. These models achieved area under the curve (AUC) values ranging from 0.71 to 0.87, sensitivity between 71% and 89%, and specificity between 40% and 70%, surpassing traditional diagnostic methods in some cases. This approach highlights the potential of retinal imaging as a key component in personalized medicine, enabling more precise risk assessment and earlier intervention. It not only aids in detecting vascular abnormalities that may precede cardiovascular events but also offers a scalable, non-invasive, and cost-effective solution for widespread screening. However, the article also emphasizes the need for further research to standardize imaging protocols and validate the clinical utility of these biomarkers across different populations. By integrating oculomics into routine clinical practice, healthcare providers could significantly enhance early detection and management of systemic diseases, ultimately improving patient outcomes. Fundus image analysis thus represents a valuable tool in the future of precision medicine and cardiovascular health management.

Revolutionizing diabetic retinopathy detection using DB-SCA-UNet with Drop Block-Based Attention Model in deep learning for precise analysis of color retinal images

Revolutionizing diabetic retinopathy detection using DB-SCA-UNet with Drop Block-Based Attention Model in deep learning for precise analysis of color retinal images

Prediction of Cardiovascular Markers and Diseases Using Retinal Fundus Images and Deep Learning: A Systematic Scoping Review

Abstract Background Rapid development in deep learning for image analysis inspired studies to focus on predicting cardiovascular risk using retinal fundus images. This scoping review aimed to identify and describe studies using retinal fundus images and deep learning to predict cardiovascular risk markers and diseases. Methods We searched MEDLINE and Embase on 17/11/2023. Abstracts and relevant full-text articles were independently screened by two reviewers. We included studies that used deep learning for the analysis of retinal fundus images to predict cardiovascular risk markers or cardiovascular diseases and excluded studies only using predefined characteristics of retinal fundus images were not considered. Study characteristics were presented using descriptive statistics. Results We included 24 articles published between 2018 and 2023. Among these, 23 (96%) were cross-sectional studies and eight (33%) were follow-up studies with clinical CVD outcomes. Seven studies included a combination of both designs. Most studies (96%) used convolutional neural networks to process images. We found nine (38%) studies that incorporated clinical risk factors in the prediction and four (17%) that compared the results to commonly used clinical risk scores in a prospective setting. Three of these reported improved discriminative performance. External validation of models was rare (21%). Conclusions There is increasing interest in using retinal fundus images in cardiovascular risk assessment with some studies demonstrating some improvements in prediction. However, more prospective studies, comparisons of results to clinical risk scores, and models augmented with traditional risk factors can strengthen further research in the field.

Automatic retinal image analysis methods using colour fundus images for screening glaucomatous optic neuropathy

ObjectivesTrain an automatic retinal image analysis (ARIA) method to screen glaucomatous optic neuropathy (GON) on non-mydriatic retinal images labelled with the additional results of optical coherence tomography (OCT) and assess...

ETDRS grading with CLARUS ultra-widefield images shows agreement with 7-fields colour fundus photography

BackgroundTo analyse and compare the grading of diabetic retinopathy (DR) severity level using standard 35° ETDRS 7-fields photography and CLARUS™ 500 ultra-widefield imaging system.MethodsA cross-sectional analysis of retinal images of patients with type 2 diabetes (n = 160 eyes) was performed for this study. All patients underwent 7-fields colour fundus photography (CFP) at 35° on a standard Topcon TRC-50DX® camera, and ultra-widefield (UWF) imaging at 200° on a CLARUS™ 500 (ZEISS, Dublin, CA, USA) by an automatic montage of two 133° images (nasal and temporal). 35° 7-fields photographs were graded by two graders, according to the Early Treatment Diabetic Retinopathy Study (ETDRS). For CLARUS UWF images, a prototype 7-fields grid was applied using the CLARUS review software, and the same ETDRS grading procedures were performed inside that area only. Grading of DR severity level was compared between these two methods to evaluate the agreement between both imaging techniques.ResultsImages of 160 eyes from 83 diabetic patients were considered for analysis. According to the 35° ETDRS 7-fields images, 22 eyes were evaluated as DR severity level 10–20, 64 eyes were evaluated as DR level 35, 41 eyes level 43, 21 eyes level 47, 7 eyes level 53, and 5 eyes level 61. The same DR severity level was achieved with CLARUS 500 UWF images in 92 eyes (57%), showing a perfect agreement (k > 0.80) with the 7-fields 35° technique. Fifty-seven eyes (36%) showed a higher DR level with CLARUS UWF images, mostly due to a better visualization of haemorrhages and a higher detection rate of intraretinal microvascular abnormalities (IRMA). Only 11 eyes (7%) showed a lower severity level with the CLARUS UWF system, due to the presence of artifacts or media opacities that precluded the correct evaluation of DR lesions.ConclusionsUWF CLARUS 500 device showed nearly perfect agreement with standard 35° 7-fields images in all ETDRS severity levels. Moreover, CLARUS images showed an increased ability to detect haemorrhages and IRMA helping with finer evaluation of lesions, thus demonstrating that a UWF photograph can be used to grade ETDRS severity level with a better visualization than the standard 7-fields images.Trial registrationApproved by the AIBILI - Association for Innovation and Biomedical Research on Light and Image Ethics Committee for Health with number CEC/009/17- EYEMARKER.

Foundation model-driven distributed learning for enhanced retinal age prediction.

The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction. The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods. Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P < .001). The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments. The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker.

Artificial intelligence-enabled profiling of overlapping retinal disease distribution for ocular diagnosis

&lt;p&gt;Eyesight, an invaluable gift profoundly impacts our daily lives. In a rapidly evolving healthcare landscape, the preservation and enhancement of ocular health stand as critical objectives. This research endeavors to analyze the two retinal fundus multi-disease image datasets (RFMiD) one containing 3200 images and the other containing 860 fundus images. The primary objective of this study is to scrutinize these datasets, discern variations in the frequency of labeled diseases within and across them, and explore common combinations of labels. These findings hold important implications for the field of retinal image analysis, as they provide valuable insights into the distribution and co-occurrence of defects.&lt;/p&gt;

An efficient explainable deep neural network classifier for diabetic retinopathy detection

Early detection of Diabetic Retinopathy (DR) is crucial for effective intervention, particularly given its often asymptomatic nature in initial stages. Automated detection systems offer significant advantages, such as improving screening efficiency, extending healthcare accessibility to remote regions, and facilitating proactive disease management. Introducing an innovative framework for retinal image analysis that combines explainable deep learning, hybrid feature extraction, and advanced data augmentation to optimize performance and interpretability for clinical applications. The xDNN model, trained on the MESSIDOR-2 dataset, achieved an average recall of 98%, precision of 98.2%, F1-score of 98%, and accuracy of 98.2%. When extensively trained on the APTOS 2019 dataset, the model delivered outstanding results with an average precision, recall, and F1-score of 99%, and an accuracy of 99.7%. Additionally, the model's performance on the IDRID dataset was remarkable, with average precision, recall, F1-score, and accuracy all reaching 99%. Noteworthy is our method impressive average Area Under the Curve (AUC) of 99.8%, affirming its consistent and exceptional performance across all classes of diabetic retinopathy. This underscores the xDNN Classifier's potential as a valuable tool for precise and reliable diabetic retinopathy detection. It holds substantial promise for elevating clinical diagnosis and enhancing treatment outcomes within the field of ophthalmology.

E-LSTM: EfficientNet and Long Short-Term Memory Model for Detection of Glaucoma Diseases

Glaucoma is an eye disease that often has no symptoms until it is advanced. According to the World Health Organization (WHO), after cataracts, glaucoma is the second-leading cause of permanent blindness globally and is expected to affect 111.8 million patients by 2040. Early detection of glaucoma is important to reduce the risk of permanent blindness. Detection is achieved by structural measurement of early thinning of the retinal nerve fiber layer (RNFL). The RNFL is the portion of the retina located outside the optic nerve head (ONH) and can be observed in fundus images of the retina. Analysis of retinal fundus images can be performed with computer assistance using machine learning, especially deep learning. This study proposes a deep learning-based model, a convolutional neural network (CNN) using the EfficientNet architecture combined with long short-term memory (LSTM), for laucoma detection. Using ACRIMA, DRISHTI-GS, and RIM-ONE DL datasets with k-fold cross-validation, the model achieved high performance on the ACRIMA dataset: accuracy 0.9799, loss 0.0596, precision 0.9802, sensitivity 0.9799, specificity 0.9771, and F1score 0.9799. This EfficientNet and LSTM combination (e-LSTM) outperformed previous studies, offering a promising alternative for evaluating retinal fundus images in glaucoma detection.

A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD).

The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep learning, and computer vision, fundamentally transforming the analysis of retinal images. By utilizing a wide array of visual cues extracted from retinal fundus images, sophisticated artificial intelligence models have been developed to diagnose various retinal disorders. This paper concentrates on the detection of Age-Related Macular Degeneration (AMD), a significant retinal condition, by offering an exhaustive examination of recent machine learning and deep learning methodologies. Additionally, it discusses potential obstacles and constraints associated with implementing this technology in the field of ophthalmology. Through a systematic review, this research aims to assess the efficacy of machine learning and deep learning techniques in discerning AMD from different modalities as they have shown promise in the field of AMD and retinal disorders diagnosis. Organized around prevalent datasets and imaging techniques, the paper initially outlines assessment criteria, image preprocessing methodologies, and learning frameworks before conducting a thorough investigation of diverse approaches for AMD detection. Drawing insights from the analysis of more than 30 selected studies, the conclusion underscores current research trajectories, major challenges, and future prospects in AMD diagnosis, providing a valuable resource for both scholars and practitioners in the domain.

Deep Learning-Based Vascular Aging Prediction From Retinal Fundus Images.

The purpose of this study was to establish and validate a deep learning model to screen vascular aging using retinal fundus images. Although vascular aging is considered a novel cardiovascular risk factor, the assessment methods are currently limited and often only available in developed regions. We used 8865 retinal fundus images and clinical parameters of 4376 patients from two independent datasets for training a deep learning algorithm. The gold standard for vascular aging was defined as a pulse wave velocity ≥1400 cm/s. The probability of the presence of vascular aging was defined as deep learning retinal vascular aging score, the Reti-aging score. We compared the performance of the deep learning model and clinical parameters by calculating the area under the receiver operating characteristics curve (AUC). We recruited clinical specialists, including ophthalmologists and geriatricians, to assess vascular aging in patients using retinal fundus images, aiming to compare the diagnostic performance between deep learning models and clinical specialists. Finally, the potential of Reti-aging score for identifying new-onset hypertension (NH) and new-onset carotid artery plaque (NCP) in the subsequent three years was examined. The Reti-aging score model achieved an AUC of 0.826 (95% confidence interval [CI] = 0.793-0.855) and 0.779 (95% CI = 0.765-0.794) in the internal and external dataset. It showed better performance in predicting vascular aging compared with the prediction with clinical parameters. The average accuracy of ophthalmologists (66.3%) was lower than that of the Reti-aging score model, whereas geriatricians were unable to make predictions based on retinal fundus images. The Reti-aging score was associated with the risk of NH and NCP (P < 0.05). The Reti-aging score model might serve as a novel method to predict vascular aging through analysis of retinal fundus images. Reti-aging score provides a novel indicator to predict new-onset cardiovascular diseases. Given the robust performance of our model, it provides a new and reliable method for screening vascular aging, especially in undeveloped areas.

Advancements in Ophthalmic Healthcare with Deep Learning-Driven Segmentation for Multi-Stage Eye Fundus Disease Diagnosis

The global rise in eye diseases highlights the need for advanced diagnostic tools in ophthalmic care. This project introduces a deep learning model for classifying eye diseases, streamlining diagnosis, and improving accuracy. Using real-time images from reputable healthcare facilities like Bajwa Hospital in Punjab and Shang gong Medical Tech in China, the model is fine-tuned to clinical nuances. Segmentation of the optic disc and blood vessels is key for precise retinal structure delineation, enhancing disease identification. Various CNN models, including Mobile Net, Dense Net, Reset, and a custom CNN, were utilized for retinal image analysis. Additionally, the Vision Transformer (ViT) model was integrated to capture intricate patterns. The model is deployed as a web application using Django, HTML, SQLite, and Bootstrap, featuring a secure, user-friendly interface. Users can input images to receive prompt disease predictions, along with verified information on prevention, treatment options, and medications. This system not only automates and improves diagnostic processes but also provides reliable medical guidance.

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.