Detection Of Glaucoma Research Articles

Effects of elevated intraocular pressure on alpha ganglion cells in experimental glaucoma mice

Glaucoma is a leading cause of blindness worldwide and glaucoma patients exhibit an early diffuse loss of retinal sensitivity followed by focal loss of RGCs. Combining some previous published results and some new data, this paper provides our current view on how high IOP (H-IOP) affects the light response sensitivity of a subset of RGCs, the alpha-ganglion cells (αGCs), as well as their presynaptic bipolar cells (DBCs and HBCs) and A2 amacrine cells (AIIACs) in dark-adapted mouse retinas. Our data demonstrate that H-IOP in experimental glaucoma mice significantly decreases light-evoked spike response sensitivity of sONαGCs and sOFFαGCs (i.e., raises thresholds by 1.5–2.5 log units), but not that of the tONαGCs and tOFFαGCs. The sensitivity loss in sONαGCs and sOFFαGCs is mediated by a H-IOP induced suppression of AIIAC response which is caused by a decrease of transmission efficacy of the DBCR→AIIAC synapse. We also provide evidence supporting the hypothesis that BK channels in the A17AC→DBCR feedback synapse are the H-IOP sensor that regulates the DBCR→AIIAC synaptic efficacy, as BK channel blocker IBTX mimics the action of H-IOP. Our results provide useful information for designing strategies for early detection and possible treatments of glaucoma as physiological changes occur before irreversible structural damage.

Glaucoma detection using hybrid machine learning techniques

An eye disease called glaucoma can cause irreversible blindness if left undetected and not properly managed. The major challenge is that glaucoma often has no symptoms in its early stages, making it hard to detect using traditional testing methods like eye pressure measurements and eye exams. Several techniques for Glaucoma detection encountered difficulties due to a short training dataset, resulting in overfitting and under fitting problems. A hybrid machine learning approach based on CNN-SVM for detection of Glaucoma was proposed. Initially images taken from glaucoma dataset were preprocessed using standard scalar, then the preprocessed images are fed into CNN for transformation into high level features, the extracted features are subsequently passed onto an SVM classifier to distinguish between normal and glaucomatous conditions. Experimental results for the proposed CNN-SVM offers an accuracy, precision, recall and F1-score of 100% demonstrating its superiority over other existing techniques such as SVM which has accuracy, precision, recall and F1-score of 93%, 92%, 90% and 94% respectively and CNN with the accuracy, precision, recall and F1- score of 95%, 99%, 88% and 90% respectively. The integration of CNN and SVM presents a promising framework for automated Glaucoma detection, offering significant potential for real-world clinical applications.

Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence

Background: If left untreated, glaucoma—the second most common cause of blindness worldwide—causes irreversible visual loss due to a gradual neurodegeneration of the retinal ganglion cells. Conventional techniques for identifying glaucoma, like optical coherence tomography (OCT) and visual field exams, are frequently laborious and dependent on subjective interpretation. Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma. Aims: The purpose of this study is to examine the current uses of AI in the early diagnosis, treatment, and detection of glaucoma while highlighting the advantages and drawbacks of different AI models and algorithms. In addition, it aims to determine how AI technologies might transform glaucoma treatment and suggest future lines of inquiry for this area of study. Methods: A thorough search of databases, including Web of Science, PubMed, and Scopus, was carried out to find pertinent papers released until August 2024. The inclusion criteria were limited to research published in English in peer-reviewed publications that used AI, ML, or DL to diagnose or treat glaucoma in human subjects. Articles were chosen and vetted according to their quality, contribution to the field, and relevancy. Results: Convolutional neural networks (CNNs) and other deep learning algorithms are among the AI models included in this paper that have been shown to have excellent sensitivity and specificity in identifying glaucomatous alterations in fundus photos, OCT scans, and visual field tests. By automating standard screening procedures, these models have demonstrated promise in distinguishing between glaucomatous and healthy eyes, forecasting the course of the disease, and possibly lessening the workload of physicians. Nonetheless, several significant obstacles remain, such as the requirement for various training datasets, outside validation, decision-making transparency, and handling moral and legal issues. Conclusions: Artificial intelligence (AI) holds great promise for improving the diagnosis and treatment of glaucoma by facilitating prompt and precise interpretation of imaging data and assisting in clinical decision making. To guarantee wider accessibility and better patient results, future research should create strong generalizable AI models validated in various populations, address ethical and legal matters, and incorporate AI into clinical practice.

Retinal Ganglion Cell Subtypes and Their Vulnerability in Glaucoma.

The detection of selective retinal ganglion cell damage in glaucoma has been a long sought-after goal, not just for the development of clinical tests for the early detection of glaucoma but for the elucidation of potential mechanisms underlying retinal ganglion cell loss. Early reports of the selective vulnerability of larger retinal ganglion cells (RGCs) in human studies did not translate simply to the loss of a particular class of RGC but more likely reflected shrinkage and degeneration across all RGC classes. Subsequent studies of nonhuman primate (NHP) models of glaucoma indicated some selectivity with great damage to the magnocellular vs parvocellular pathways. More recently, rodent models of experimental glaucoma have highlighted a selective vulnerability of OFF-centered RGCs-particularly those with transient responses. Selectivity for OFF pathway damage is also seen as a trend in a rat model of glaucoma. These data support the concept that some RGCs are more vulnerable to the effects of glaucoma damage. This chapter covers some of the methods to elucidate RGC damage and the relevance of model selection to mimic human glaucoma rather than just RGC death.

Self-Organized Operational Neural Networks for Early Glaucoma Diagnosis in Digital Fundus Images

Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational Neural Networks (Self-ONNs) are proposed for early detection of glaucoma in fundus images and their performance is compared against the conventional (deep) Convolutional Neural Networks (CNNs) over three benchmark datasets: ACRIMA, RIM-ONE, and ESOGU. The experimental results demonstrate that Self-ONNs not only achieve superior detection performance but can also significantly reduce the computational complexity making it a potentially suitable network model for biomedical datasets especially when the data is scarce.

Erratum — Optimized Improved Random Forest-Fostered Glaucoma Detection from Fundus Retinal Images

Erratum — Optimized Improved Random Forest-Fostered Glaucoma Detection from Fundus Retinal Images

AN INTERPRETABLE GLAUCOMA DETECTION USING DUAL SCALE CROSS-ATTENTION VISION TRANSFORMER-BASED LONG SHORT TERM MEMORY WITH OPTICAL CUP AND DISK SEGMENTATION

Glaucoma is a kind of eye disease that tends to generate harm to the optic nerve. It is a neurodegenerative illness, which develops intraocular hypertension because of its maximized aqueous humor and blockage between the cornea and iris. It causes destruction to the optic nerve head, which transfers visual stimulus to the brain from the eyes. This results in loss of visual field and blindness. For vision, glaucoma is known to be a sneak thief due to its complexity in detecting it in the early stage. It requires continuous screening to determine the neurological disorder. Effective identification of glaucoma requires more cost and time, but it also causes human error in the detection phase based on resource availability. The problems based on the robustness of the algorithm are not solved in the earlier method especially relative to that human expert counterpart. Therefore, effective glaucoma detection with the help of deep learning is developed to recognize eye disease in the early stage. At first, the input eye images are taken from the available sites. Subsequently, the procedure for segmentation is done using the Optimized Dilated Mobile-Unet[Formula: see text] (ODMUnet[Formula: see text]) to segment the optic disc and optic cup in the input images. Here, the parameters in the developed ODMUnet[Formula: see text] are optimized using an Improved Drawer Algorithm (IDrA). The segmented “optic disc and optic cup” images are given to the developed Dual Scale Cross-Attention Vision Transformer-based Long Short-Term Memory (DSCAViT-LSTM) for glaucoma detection. The experimental outcomes of the recommended model are evaluated with other deep learning techniques to ensure its efficacy.

Explainable deep learning for glaucoma detection: Efficient dense net with quantum-based Dwarf Mongoose optimization

Glaucoma, a progressive eye disease, leads to irreversible vision loss due to optic nerve damage. This study aims to develop a more explainable and interpretable deep learning-based model for the detection and classification of glaucoma, addressing the current limitations in the field. The proposed approach utilizes a modified DenseNet-201 architecture, incorporating an additional transition layer for enhanced glaucoma classification. A pre-trained Efficient DenseNet is applied, and a reweighted cross-entropy loss function is used to handle the class imbalance in the training data. Quantum-based Dwarf Mongoose Optimization (QDMOA) is employed to fine-tune the model’s parameters, minimizing over fitting and improving generalization on small datasets. Experimental results demonstrate that the proposed method effectively detects and classifies two types of glaucoma. The use of dense connections with regularization significantly reduces over fitting, and the reweighted loss function improves model performance on imbalanced data. The developed deep learning model offers improved accuracy in glaucoma detection and classification while addressing the need for more interpretable and reliable models in medical applications. This research provides a practical tool for automated glaucoma screening, which could support ophthalmologists in early diagnosis and treatment, reducing the risk of irreversible vision loss.

Anatomical compensation of retinal nerve fiber layer improves the detection of glaucoma between ethnicities.

The study aimed to evaluate the impact of compensating retinal nerve fiber layer (RNFL) thickness for demographic and anatomical factors on glaucoma detection in Chinese and Indian adults. A population-based study included 1995 healthy participants (1076 Chinese and 919 Indians) to construct a multivariable linear regression compensation model. This model was applied to 357 Chinese glaucoma patients, 357 healthy Chinese, and 357 healthy Indians using Cirrus spectral-domain optical coherence tomography (OCT). The compensated RNFL thickness considered age, refractive error, optic disc parameters, and retinal vessel density. Results showed that although the average RNFL thickness was significantly higher in Chinese participants compared to Indians, the compensation model reduced this difference to nonsignificance. Moreover, the compensation model significantly improved the area under the receiver operating characteristic curve (0.90 vs. 0.78; p<0.001), sensitivity (75% vs. 51%), and specificity (67% vs. 32%) in distinguishing Chinese glaucoma patients from healthy Indian individuals. The compensation model significantly enhanced the diagnostic accuracy of RNFL thickness in distinguishing glaucoma in the Chinese ethnic group compared to the OCT instrument's default values. These results suggest that modifying RNFL measurements based on individual characteristics can yield substantial benefits for glaucoma detection across ethnicities.

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.

Multi-GlaucNet: A multi-task model for optic disc segmentation, blood vessel segmentation and glaucoma detection

Glaucoma is a common and severe ocular disease that often leads to vision loss. The information on the optic disc (OD) and blood vessels in fundus images can significantly aid in glaucoma detection. In addition, the use of deep learning models for glaucoma detection is a highly effective approach. We propose a multi-task deep learning model called Multi-GlaucNet that can simultaneously segment the OD and blood vessels, thereby assisting doctors in diagnosing glaucoma. Multi-GlaucNet consists of three modules: the OD segmentation module, blood vessel segmentation module and glaucoma detection module. The OD segmentation module and blood vessel segmentation module both adopt an encoder–decoder structure to segment OD and blood vessels, respectively. The segmentation module is constructed using bottleneck layers in the encoding process and uses Pixel Shuffle and channel attention mechanisms in the decoding process. The detection module uses the ResNet50 network to perform glaucoma detection based on the features extracted from the segmentation modules. Multi-GlaucNet demonstrates outstanding performance in three areas. It achieves a Dice coefficient of 96.7% for OD segmentation on the ORIGA dataset, accuracy of 0.9798 and F1 score of 0.8562 for blood vessel segmentation on a mixed dataset. For glaucoma detection on the REFUGE dataset, it attains the highest accuracy of 0.967 and an area under the curve (AUC) of 0.950. These results validate the effectiveness of Multi-GlaucNet for glaucoma detection. The model’s ability to perform multiple tasks with high accuracy and efficiency demonstrates that it is a valuable aiding tool for glaucoma diagnosis.

Discrete ripplet-II transform feature extraction and metaheuristic-optimized feature selection for enhanced glaucoma detection in fundus images using least square-support vector machine

Discrete ripplet-II transform feature extraction and metaheuristic-optimized feature selection for enhanced glaucoma detection in fundus images using least square-support vector machine

Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images

Glaucoma, a leading cause of permanent blindness worldwide, necessitates early detection to prevent vision loss, a task that is challenging and time-consuming when performed manually. This study proposes an automatic glaucoma detection method on enhanced retinal images using deep learning. The system analyzes retinal images, generating masks for the optic disc and optic cup, and providing a classification for glaucoma diagnosis. We employ a U-Net architecture with a pretrained residual neural network (ResNet34) for segmentation and an EfficientNetB0 for classification. The proposed framework is tested on publicly available datasets, including ORIGA, REFUGE, RIM-ONE DL, and HRF. Our work evaluated the U-Net model with five pretrained backbones (ResNet34, ResNet50, VGG19, DenseNet121, and EfficientNetB0) and examined preprocessing effects. We optimized model training with limited data using transfer learning and data augmentation techniques. The segmentation model achieves a mean intersection over union (mIoU) value of 0.98. The classification model shows remarkable performance with 99.9% training and 100% testing accuracy on ORIGA, 99.9% training and 99% testing accuracy on RIM-ONE DL, and 98% training and 100% testing accuracy on HRF. The proposed model outperforms related works and demonstrates potential for accurate glaucoma classification and detection tasks.

Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA

Glaucoma is a group of neurodegenerative diseases that can lead to irreversible blindness. Yet, the progression can be slowed down if diagnosed and treated early enough. Optical coherence tomography angiography (OCTA) can non-invasively provide valuable information about the retinal microcirculation that has shown to be correlated with the onset of the disease. The vessel density (VD) is the most commonly used biomarker to quantify this vascular information. However, different studies showed that there is a great impact of the acquisition area on the performance of the VD to distinguish between glaucoma patients and a healthy control group. It also seems that the separate capillary plexuses are differently affected by the disease and therefore also influence the results. So in this study we investigate the impact of the acquisition area (3 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 3 mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{mm}$$\\end{document} macular scan, 6.44 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 6.4 mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{mm}$$\\end{document} macular scan, 6 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 6 mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{mm}$$\\end{document} optic nerve head (ONH) scan) and the different plexuses on the machine-learning-based distinction between glaucoma patients and healthy controls. The results yielded that the 6 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} 6 mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{mm}$$\\end{document} ONH show the best performance over all plexuses. Moreover the deep learning-based approach outperforms the VD as a biomarker on every acquisition area and plexus. In addition to that, it also performs better than traditional biomarkers obtained from the OCT scans that are used in the clinical routine for diagnosis and progression tracking of glaucoma. Consequently, OCTA scans of the ONH might be a useful addition to OCT when studying glaucoma.

Evaluating a foundation AI model for glaucoma detection in a diverse study population using color fundus photographs

PurposeTo evaluate RETFound, a foundation AI model, using a diverse clinical research dataset to assess its accuracy in detecting glaucoma using optic disc photographs. The model's accuracy for glaucoma detection was evaluated across race, age, glaucoma severity and various training cycles (epochs) and dataset sample sizes. DesignEvaluation of a diagnostic technology Subjects, Participants, and ControlsThe study included 9787 color fundus photographs (CFP) from 2329 participants of diverse race (White (73.3%), Black (13.6%) and other (13%) , disease severity (21.8% mild glaucoma, 7.2% moderate or advanced glaucoma, 60.3% not glaucoma, and 10.7% unreported), and age (48.8% <60 years, 51.1% > 60 years) from the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES). All fundus photographs were graded as "Glaucomatous" or "Non-glaucomatous". MethodsThe study employed RETFound, a self-supervised learning model, to perform binary glaucoma classification. The diagnostic accuracy of RETFound was iteratively tested across different combinations of dataset sample sizes (50 to 2000 optic disc photographs), training cycles (5 to 50), and study subpopulations stratified by severity of glaucoma, age and race). Main Outcome MeasuresDiagnostic Accuracy (AUC) for classifying CFP as "Glaucomatous" or "Non-glaucomatous". ResultsPerformance increased with larger training datasets and more training cycles, improving from50 training images and 5 epochs (AUC: 0.52) to 2,000 training images and 50 epochs (AUC: 0.86), with reduced gain in performance from approximately 500 and 1000 training images (AUC of 0.82 and 0.83, respectively). Performance was consistent across race and age for all training size and cycle number combinations: Black (AUC=0.87) vs other (AUC=0.86), and >60 years (AUC=0.84) vs <60 years (AUC=0.87). Performance was significantly higher in patients with moderate to severe vs mild glaucoma (AUC=0.95 vs 0.84, respectively). ConclusionsGood RETFound performance was observed with a relatively small sample size of optic disc photographs used for fine tuning and across differences in race and age. RETFound’s ability to adapt across a range of CFP training conditions and populations suggests it is a promising tool to automate glaucoma detection in a variety of use cases.

Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting

PurposeThis study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eyecare setting, using non-mydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). AI results were compared with tele-ophthalmologists' diagnoses and with a glaucoma specialist's assessment for those participants referred to tertiary eyecare hospital. DesignProspective, cross-sectional study Participants303 participants from 6 satellite vision centers of a tertiary eye hospital MethodsAt the vision center, participants underwent comprehensive eye evaluations, including clinical history, visual acuity measurement, slit lamp examination, intraocular pressure measurement, and fundus photography using the FOP NM-10 camera. Medios AI-Glaucoma software analysed 42-degrees disc-centric fundus images, categorizing them as normal, glaucoma, or suspect. Tele-ophthalmologists who were glaucoma fellows with a minimum of 3 years of ophthalmology and 1 year of glaucoma fellowship training, masked to AI results, remotely diagnosed subjects based on the history and disc appearance. All participants labelled as disc suspects or glaucoma by AI or tele-ophthalmologists underwent further comprehensive glaucoma evaluation at the base hospital, including clinical examination, Humphrey visual field analysis (HFA), and Optical Coherence Tomography (OCT). AI and tele-ophthalmologist diagnoses were then compared with a glaucoma specialist's diagnosis. Main Outcome MeasuresSensitivity and Specificity of Medios AI ResultsOut of 303 participants, 299 with at least one eye of sufficient image quality were included in the study. The remaining 4 participants did not have sufficient image quality in both eyes. Medios AI identified 39 participants (13%) with referable glaucoma. The AI exhibited a sensitivity of 0.91 (95% CI: 0.71 - 0.99) and specificity of 0.93 (95% CI: 0.89 - 0.96) in detecting referable glaucoma (definite perimetric glaucoma) when compared to tele-ophthalmologist. The agreement between AI and the glaucoma specialist was 80.3%, surpassing the 55.3.% agreement between the tele-ophthalmologist and the glaucoma specialist amongst those participants who were referred to the base hospital. Both AI and the tele-ophthalmologist relied on fundus photos for diagnoses, while the glaucoma specialist's assessments at the base hospital were aided by additional tools such as HFA and OCT. Furthermore, AI had fewer false positive referrals (2 out of 10) compared to the tele-ophthalmologist (9 out of 10). ConclusionMedios offline AI exhibited promising sensitivity and specificity in detecting referable glaucoma from remote vision centers in southern India when compared with teleophthalmologists. It also demonstrated better agreement with glaucoma specialist’s diagnosis for referable glaucoma participants.

Celebration to Consequence: Case Report of Late Onset Ocular Effects of Firework Injuries

Firework-related injuries, particularly ocular trauma, pose a significant public health concern during cultural celebrations. We explore the case of a 20-year-old male who sustained firework-induced ocular trauma at the age of seven, subsequently developing traumatic cataract and glaucomatous optic atrophy. The patient presented with decreased vision and pain in the left eye (LE), 13 years post-injury. He underwent cataract surgery nine years after the incident, with subsequent development of glaucomatous optic atrophy. Examination revealed visual acuity impairment, exotropia, and pupil abnormalities in the LE, along with scleral thinning and corneal opacity. Posterior segment evaluation showed optic nerve cupping and hemorrhages. Gonioscopy indicated angle recession and synechiae in the LE. Optical coherence tomography revealed retinal nerve fiber layer defects. Elevated intraocular pressure in the LE was successfully managed with anti-glaucoma medication. Firework-related ocular trauma can lead to long-term complications, including traumatic cataract and glaucomatous optic atrophy. Timely cataract surgery and vigilant follow-up are essential for early detection and management of secondary glaucoma. Regulatory measures to mitigate firework-related injuries are imperative to prevent such incidents and preserve visual health.

CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images.

Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed to tackle the challenge of early glaucoma detection. Nevertheless, limited approaches have been suggested to improve glaucoma classification due to issues like inadequate training data, variations in feature distribution, and the overall quality of samples. Furthermore, fundus images display significant similarities and slight discrepancies in lesion sizes, complicating glaucoma classification when utilizing ViTs. To address these obstacles, we introduce the contour-guided and augmented vision transformer (CA-ViT) for enhanced glaucoma classification using fundus images. We employ a Conditional Variational Generative Adversarial Network (CVGAN) to enhance and diversify the training dataset by incorporating conditional sample generation and reconstruction. Subsequently, a contour-guided approach is integrated to offer crucial insights into the disease, particularly concerning the optic disc and optic cup regions. Both the original images and extracted contours are given to the ViT backbone; then, feature alignment is performed with a weighted cross-entropy loss. Finally, in the inference phase, the ViT backbone, trained on the original fundus images and augmented data, is used for multi-class glaucoma categorization. By utilizing the Standardized Multi-Channel Dataset for Glaucoma (SMDG), which encompasses various datasets (e.g., EYEPACS, DRISHTI-GS, RIM-ONE, REFUGE), we conducted thorough testing. The results indicate that the proposed CA-ViT model significantly outperforms current methods, achieving a precision of 93.0%, a recall of 93.08%, an F1 score of 92.9%, and an accuracy of 93.0%. Therefore, the integration of augmentation with the CVGAN and contour guidance can effectively enhance glaucoma classification tasks.

Optimized Improved Random Forest-Fostered Glaucoma Detection from Fundus Retinal Images

Glaucoma is a major cause of irreversible blindness caused by optic nerve damage. The ophthalmologist uses retinal examination of the dilated pupil to diagnose this disease. Since this diagnosis is a manual and laborious procedure, an automated technique is required for faster diagnosis. Automated retinal image processing is deemed a competitive research field owing to its lower accuracy results, complication and improper effects related with it. Therefore, Optimized Improved Random Forest fostered Glaucoma Detection from Fundus Retinal Images (IRF-MOSOA-GD) is proposed in this paper. Here, Images are acquired through the datasets of DRISHTI-GS, ORIGA and RIM_ONE and given to the pre-processing. The pre-processing is carried out utilizing the Savitzky–Golay Denoising technique for eliminating the noise at the input images. Then the pre-processed image is given to the feature extraction phase. In the feature extraction phase, the region features are extracted with the help of the Fuzzy color and Texture histogram (FCTH), Edge histogram and Pyramid Histograms of Orientation Gradients (PHOG) method. Then, the extracted feature is fed to the Improved Random Forest (IRF) classifier for categorizing the normal and Glaucoma images. The hyperparameter of the IRF classifier is tuned with a Multi-Objective Squirrel Optimization Algorithm (MOSOA) to attain better categorization of normal and glaucoma images. The proposed technique is implemented in Java and its efficiency is analyzed under some metrics, like accuracy, F-scores and computational time. The IRF-MOSOA-GD method attains higher accuracy in the DRISHTI-GS dataset at 23.6%, 27.55% and 24.98% higher accuracy compared with existing techniques.

An improved ensemble deep learning framework for glaucoma detection

An improved ensemble deep learning framework for glaucoma detection