Detection Of Glaucoma Research Articles

Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Glaucoma Using Machine Learning.

Machine learning (ML) based on the optical coherence tomography angiography vessel density features with different thresholds using a support vector machine (SVM) model provides excellent performance for glaucoma detection. To assess the classification performance of ML based on the 4 vessel density features of peripapillary optical coherence tomography angiography for glaucoma detection. Images from 119 eyes of 119 glaucoma patients and 76 eyes of 76 healthy individuals were included. Four vessel density features of optical coherence tomography angiography images were developed using a threshold-based segmentation method and were integrated into 3 models of machine learning classifiers. Images were divided into 70% training set and 30% test set. Classification performances of SVM, random forest, and Gaussian Naive Bayes models were evaluated with the area under the receiver operating characteristic curve (AUC) and accuracy. Glaucoma eyes had lower vessel densities at different thresholds. For differentiating glaucoma eyes, the best results were achieved with 70% and 100% thresholds, in which SVM classifier discriminated glaucoma from healthy eyes with an AUC of 1 and accuracy of 1. After SVM, the random forest classifier with 100% thresholds showed an AUC of 0.993 and an accuracy of 0.994. Furthermore, the AUC of our ML performance (SVM) was 0.96 in a subgroup analysis of mild and moderate glaucoma eyes. ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for glaucoma detection.

An investigation of Glaucoma Detection in RFI Using Bio-Inspired Multivariable FE with ML Models for Early Diagnosis

To propose a bio-inspired multi-variant feature extraction technique in retinal fundus images to improve glaucoma detection for easy diagnosis. To improve the classification accuracy of glaucoma disease, a machine learning approach is employed based on the features extracted in RFI. Methods: The Fish School Search MOFS (FSS-MOFS) bio-inspired technique is employed to extract the multivariable features from digital RFI and spot the glaucoma variations to maximize the discrimination power and minimize the dimensionality. The Binary Robust Independent Elementary Feature (BRIEF-LD) technique is employed to identify the latent spots in RFI. The PAPILA dataset is utilized for this research study, which includes the clinical records of 244 patients and 488 images of the right and left eye of all the patients under 2 categories (male and female) with results in healthy, glaucoma, and suspect categories. In order to perform deep pixel identification and RFI segmentation, the Densenet-121 architecture is used along with a deep-lab model to incarcerate the built-in fine points of RFI with the help of dilated convolutions. The MATLAB tool is used to assess the suggested bio-inspired technique, and comparative analysis is done against the prevailing glaucoma detection and feature extraction methods such as I-SVM, RFSO, SE-GSO, VGG-Net, and DBN classifiers. Findings: The suggested FSS-MOFS multi-variant feature extraction technique outperforms the prevailing ML methods such as I-SVM, RFSO, SE-GSO, VGG-Net, and DBN classifiers. The result of the comparative study shows 94% accuracy, 96% sensitivity, 97% specificity, 96% F-measure, 96.8% Precision, 97% recall, 95% detection speed, 92.06% TPR, 92.15% TNR, and 8% FPR & FNR. Novelty: The promising result of the FSS-MOFS method boosts the accuracy level of glaucoma detection and classification compared to other classification techniques. The suggested model helps the ophthalmologists identify and diagnose glaucoma in a robust way. FSS-MOFS overcomes the shortcomings of prevailing feature extraction methods.

Racial Differences in the Diagnostic Accuracy of OCT Angiography Macular Vessel Density for Glaucoma

To evaluate and compare the diagnostic accuracy of macular vessel density (VD) measured by OCT angiography (OCTA) in individuals of African descent (AD) and European descent (ED) with open-angle glaucoma. Observational, cross sectional study. A total of 176 eyes of 123 patients with glaucoma and 140 eyes of 88 healthy participants from the Diagnostic Innovations in Glaucoma Study. Whole-image ganglion cell complex (wiGCC) thickness and macular VD (parafoveal VD and perifoveal VD) were obtained from 6× 6 macula scans. Area under the receiver operating characteristic (AUROC) curves were used to evaluate the diagnostic accuracy of macular VD and ganglion cell complex (GCC) thickness in AD and ED participants after adjusting for confounders such as age, visual field mean deviation (VF MD), signal strength index, axial length, self-reported hypertension and diabetes. Macular VD and wiGCC measurements. Parafoveal and perifoveal VD were significantly lower in ED than AD patients with glaucoma. Parafoveal and perifoveal VD performed significantly worse in AD participants compared with ED participants for detection of glaucoma (adjusted AUROC, 0.75 [95% confidence interval (CI), 0.62, 0.87], 0.85 [95% CI, 0.79, 0.90], P= 0.035; and 0.82 [95% CI, 0.70, 0.92], 0.91 [95% CI, 0.87, 0.94], respectively; P= 0.020). In contrast to VD, diagnostic accuracy of GCC thickness was similar in AD and ED individuals (adjusted AUROC, 0.89 [95% CI, 0.79, 0.96], 0.92 [95% CI, 0.86, 0.96], respectively; P= 0.313). The diagnostic accuracies of both macular VD and GCC thickness for differentiating between glaucoma and healthy eyes increased with increasing VF MD in both AD and ED participants. Diagnostic performance of OCTA macular VD, but not GCC thickness, for glaucoma detection varies by race. Moreover, macular VD parameters had lower accuracy for detecting glaucoma in AD individuals than in ED individuals. The diagnostic performance of macular VD is race-dependent, and, therefore, race should be taken into consideration when interpreting macular OCTA results. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Comparing Diagnostic Accuracy of MRW and RNFL in Detection of Glaucoma

Purpose: To compare diagnostic accuracy of Bruch’s membrane opening minimum rim width (BMO-MRW) and Retinal Nerve Fibre Layer (RNFL) in detection of glaucoma taking cup to disc ratio as gold standard. Study Design: Descriptive observational. Place and Duration of Study: Liaquat National Hospital, Karachi from September 2017 to February 2018. Methods: Total 344 participants including healthy and glaucoma suspect (according to the cup disc ratio or high intra ocular pressure more than 21 mmHg) were included. They were evaluated to compare diagnostic accuracy of MRW and RNFL taking cup to disc ratio as gold standard. Spectral domain OCT was performed. Sensitivity, specificity, and diagnostic accuracy of MRW and RNFL were calculated keeping cup-disc finding as gold standard. Results: Mean Cup to Disk Ratio was 0.54 ± 0.10. Total 97.4% cases were diagnosed to have Glaucoma by Cup to Disk Ratio, 84.3% cases were diagnosed with Glaucoma by RNFL and 94.8% cases by MRW. By RNFL, 288 patients were true positive and 7 patients were true negative. Sensitivity, Specificity, and accuracy were 86.0%, 77.8%, and 85.7% respectively. The results showed that by MRW, 324 patients were true positive, and 7 patients were true negative. Sensitivity, Specificity, and accuracy were 96.7%, 77.8%, and 96.2% respectively. Conclusion: The sensitivity of RNFL for diagnosis of glaucoma is higher than MRW. While the specificity of both RNFL and MRW was similar.

Extracting decision-making features from the unstructured eye movements of clinicians on glaucoma OCT reports and developing AI models to classify expertise.

This study aimed to investigate the eye movement patterns of ophthalmologists with varying expertise levels during the assessment of optical coherence tomography (OCT) reports for glaucoma detection. Objectives included evaluating eye gaze metrics and patterns as a function of ophthalmic education, deriving novel features from eye-tracking, and developing binary classification models for disease detection and expertise differentiation. Thirteen ophthalmology residents, fellows, and clinicians specializing in glaucoma participated in the study. Junior residents had less than 1 year of experience, while senior residents had 2-3 years of experience. The expert group consisted of fellows and faculty with over 3 to 30+ years of experience. Each participant was presented with a set of 20 Topcon OCT reports (10 healthy and 10 glaucomatous) and was asked to determine the presence or absence of glaucoma and rate their confidence of diagnosis. The eye movements of each participant were recorded as they diagnosed the reports using a Pupil Labs Core eye tracker. Expert ophthalmologists exhibited more refined and focused eye fixations, particularly on specific regions of the OCT reports, such as the retinal nerve fiber layer (RNFL) probability map and circumpapillary RNFL b-scan. The binary classification models developed using the derived features demonstrated high accuracy up to 94.0% in differentiating between expert and novice clinicians. The derived features and trained binary classification models hold promise for improving the accuracy of glaucoma detection and distinguishing between expert and novice ophthalmologists. These findings have implications for enhancing ophthalmic education and for the development of effective diagnostic tools.

Adaptive cluster-based superpixel segmentation and BMWMMBO-based DCNN classification for glaucoma detection

Adaptive cluster-based superpixel segmentation and BMWMMBO-based DCNN classification for glaucoma detection

Association Between Retinal Nerve Fiber Layer Thickness and Incident Dementia in the European Prospective Investigation into Cancer in Norfolk Cohort.

Retinal nerve fiber layer (RNFL) thickness may reflect cerebral status. This study assessed the relationship between RNFL thickness and incident all-cause dementia in the European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) Eye Study. Glaucoma detection with variable corneal compensation (GDx-VCC) and Heidelberg Retinal Tomograph II (HRT II) derived global mean RNFL thickness from dementia-free participants at baseline within the EPIC-Norfolk Eye Study were analyzed. Incident dementia was identified through linkage to electronic medical records. Cox proportional hazard mixed-effects regression models adjusted for key confounders were used to examine the associations between RNFL thickness and incident dementia in four separate models. 6,239 participants were included with 322 cases of incident dementia and mean age of 67.5-years old, with 49.7% women (median follow-up 13.2-years, interquartile range (11.7 to 14.6 years). Greater RNFL thickness (GDx-VCC) was not significantly associated with a lower risk of incident dementia in the full adjusted model [HR per quartile increase 0.95; 95% CI 0.82-1.10]. Similarly, RNFL thickness assessed with HRT II was also not associated with incident dementia in any model (full adjusted model; HR per quartile increase: 1.06; [95% CI 0.93-1.19]. Gender did not modify any associations under study. GDx-VCC and HRT II derived RNFL thickness are unlikely to be useful predictors of incident dementia. Higher resolution optical imaging technologies may clarify whether there are useful relationships between neuro-retinal morphology and brain measures.

Reliability of Measurements Using Ocular Response Analyzer as a Screening Tonometer and Corneal Hysteresis Values in the Presence or Absence of Glaucomatous Changes in Fundus.

Use of the Ocular Response Analyzer (ORA) as a screening tonometer in clinical practice yielded reliable measurements in over 80% of eyes screened. Including corneal hysteresis (CH) data in screening may improve the accuracy of glaucoma detection. To examine measurement reliability when the ORA is used as a screening tonometer, and to compare CH measurements in eyes with and those without glaucomatous changes in the fundus. 1488 eyes of 747 patients (mean age: 53.5 ± 20.4y, range: 6-94y) underwent intraocular pressure (IOP) measurement using ORA as screening. The percentage of eyes with a waveform score ≥6, the recommended threshold indicating reliability, was calculated. Eyes that had waveform score ≥6 and had undergone fundus photography and optical coherence tomography were assessed for the presence or absence of glaucomatous changes in fundus from optical coherence tomography and fundus images, and CH was compared between the 2 groups. Mean ± SD (range) of ORA measurements were: Goldmann-correlated IOP 14.9 ± 4.8 (1.0-63.2)mmHg, corneal-compensated IOP 16.2 ± 4.7 (3.2-73.6)mmHg, CH 9.7 ± 1.5 (0.0-20.6)mmHg, and waveform score 7.3 ± 1.5 (0.1-9.7). Eighty-four percent of eyes had a waveform score ≥6. Among 192 eyes (127 patients, aged 53.5 ± 18.0y) with waveform score ≥6 and evaluable for glaucomatous changes in the fundus, 53 eyes were determined as positive and 139 eyes as negative. CH was 9.6 ± 1.4 (6.8-13.3)mmHg in the positive group and 10.2 ± 1.2 (6.9-13.3)mmHg in the negative group, and was significantly lower in the positive group ( P =0.003). When using ORA as a screening tonometer, reliable results were obtained in ~80% of the eyes. CH was lower in the glaucomatous change-positive group compared with the glaucomatous change-negative group, but the ranges overlapped between the 2 groups.

Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma.

Ophthalmic images, along with their derivatives like retinal nerve fiber layer (RNFL) thickness maps, play a crucial role in detecting and monitoring eye diseases such as glaucoma. For computer-aided diagnosis of eye diseases, the key technique is to automatically extract meaningful features from ophthalmic images that can reveal the biomarkers (e.g., RNFL thinning patterns) associated with functional vision loss. However, representation learning from ophthalmic images that links structural retinal damage with human vision loss is non-trivial mostly due to large anatomical variations between patients. This challenge is further amplified by the presence of image artifacts, commonly resulting from image acquisition and automated segmentation issues. In this paper, we present an artifact-tolerant unsupervised learning framework called EyeLearn for learning ophthalmic image representations in glaucoma cases. EyeLearn includes an artifact correction module to learn representations that optimally predict artifact-free images. In addition, EyeLearn adopts a clustering-guided contrastive learning strategy to explicitly capture the affinities within and between images. During training, images are dynamically organized into clusters to form contrastive samples, which encourage learning similar or dissimilar representations for images in the same or different clusters, respectively. To evaluate EyeLearn, we use the learned representations for visual field prediction and glaucoma detection with a real-world dataset of glaucoma patient ophthalmic images. Extensive experiments and comparisons with state-of-the-art methods confirm the effectiveness of EyeLearn in learning optimal feature representations from ophthalmic images.

The VARIATION OF THE AXIAL LOCATION OF BRUCH’S MEMBRANE OPENING, GANGLION CELL INNER PLEXIFORM LAYER AND FLUX INDEX IN NORMAL TENSION GLAUCOMA AND PRIMARY OPEN ANGLE GLAUCOMA PATIENTS

Introduction. Bruch’s membrane opening (BMO) at the most anterior point of the neural canal for quantification of cup depth /based on retinal surface position, area and volume, laminar position, and prelaminar connective tissue volume. If these deep structural parameters are to be used effectively in glaucoma detection and identification of progression. Purpose. This study explores variation in the axial location of Bruch’s membrane opening (BMO) to determine if this reference plane varies with NTG and POAG patients. Methods This was a cross-sectional prospective descriptive study with purposive sampling. This study enrolled 72 eyes in Sardjito General Hospital underwent cirrus – OCT 5000 Ver 11.0.0.29946 between 1st March 2022 and 10th May 2022. Results. We evaluated 72 (25 NTG and 47 POAG) eyes on 37 outpatients service of Sardjito General Hospital. Median BMO, GCIPL, Flux index and CD ratio of NTG patients was 1.707 µm (1.387-2.182), 83,0µm (76-88), 0,40 (0.27-0,48), 0,64 (0,22-0,75). Median BMO, GCIPL, Flux index and CD ratio of POAG patients was 1.612 (1.028-2.031) µm, 79,00 (56-98) µm, 0,40 (0,26-0,47) and 0,64 (0,28-0,92) for CDR. Bruch’s membrane opening position was more posterior relative to the sclera in older subjects with negative correlation (P value = 0,07). Variable BMO and GCIPL tested between NTG and POAG group showed significant difference with BMO P value =0,04 and GCIPL P value=0,01 except Perfusion, flux index and RNFL. Conclusion. Bruch’s membrane opening is more posteriorly located in older individuals. There was significant difference p value of BMO between the mean of NTG and POAG.

Genetically Optimized Neural Network for Early Detection of Glaucoma and Cardiovascular Disease Risk Prediction

Genetically Optimized Neural Network for Early Detection of Glaucoma and Cardiovascular Disease Risk Prediction

English

English

Multi-Stage CNN: U-Net and Xcep-Dense of Glaucoma Detection in Retinal Images

Glaucoma is a chronic neurological disease in the human eye where there is damage to the nerves which causes vision loss to blindness. Glaucoma can be detected by classifying retinal images. Several previous studies that classified glaucoma did not perform segmentation beforehand. Segmentation is needed to extract the features of the optic disc and optic cup from retinal images that are used to detect glaucoma. This study proposes two stages in the detection of glaucoma, namely the segmentation and classification stages. Segmentation is carried out using the U-Net architecture. Classification is done using a new architecture, namely Xcep-Dense. The Xcep-Dense architecture is a new architecture which is the result of a combination of the Xception and DenseNet architectures. At the segmentation stage, accuracy, recall, precision, and F1-score values are obtained above 90%. The Cohen’s kappa value has a value above 85% and loss below 20%. At the classification stage, accuracy and specification values were obtained above 85%, sensitivity and F1-score above 80%, and Cohen’s kappa above 70%. The predicted image obtained at the segmentation stage has a very similar appearance to the ground truth. Based on the results of the performance evaluation obtained, it shows that the method proposed in this study is feasible in detecting glaucoma.Glaucoma,

Sensor data analysis and development of machine learning models for detection of glaucoma

This paper focuses on analysis of data acquired using Triggerfish contact lens sensor and devices for continuous monitoring of cardiovascular system properties. One of the aims of our research is to build machine learning models that can be applied in clinical practice to detect glaucoma independently of currently used imaging techniques. We show that cardiac sensor data derived attributes are complementary to Triggerfish data and improve mean AUC estimates of the predictive models. We propose methods of attribute generation for raw data recorded by the devices. During data preprocessing we consider division of 24-hour monitoring period for time intervals depending on physiological circadian cycle properties. Comprehensive comparison of model predictive performance metrics is included. Our predictive model involving measurements of corneal biomechanical properties and sensor data based attributes provides AUC of 0.87. It can support glaucoma detection without direct intraocular pressure measurement. We also refer to the issues related to personalized medicine and application of machine learning techniques.

Neurovascular segregation of the retinal nerve fiber layer in glaucoma.

The imaging data of one eye from 154 healthy and 143 glaucoma participants were acquired to evaluate the contributions of the neuronal and vascular components within the retinal nerve fiber layer (RNFL) for detecting glaucoma and modeling visual field loss through the use of optical coherence tomography (OCT) and OCT angiography. The neuronal and vascular components within the circumpapillary RNFL were independently evaluated. In healthy eyes, the neuronal component showed a stronger association with age (r = -0.52, p < 0.001) compared to measured RNFL thickness (r = -0.46, p < 0.001). Using the neuronal component alone improved detection of glaucoma (AUC: 0.890 ± 0.020) compared to measured RNFL thickness (AUC: 0.877 ± 0.021; χ2 = 5.54, p = 0.019). Inclusion of the capillary components with the sectoral neuronal component resulted in a significant improvement in glaucoma detection (AUC: 0.927 ± 0.015; χ2 = 15.34, p <0.001). After adjusting for potential confounders, AUC increased to 0.952 ± 0.011. Results from modeling visual field loss in glaucoma eyes suggest that visual field losses associated with neuronal thinning were moderated in eyes with a larger capillary component. These findings suggest that segregation of the neurovascular components could help improve understanding of disease pathophysiology and affect disease management in glaucoma.

Relative Importance of Glaucoma-Referral Indicators in Retinal Images in a Diabetic Retinopathy Screening Programme in Slovenia: A Cross-Sectional Study.

Background and Objectives: Glaucoma is a major cause of irreversible visual impairment and blindness, so its timely detection is crucial. Retinal images from diabetic retinopathy screening programmes (DRSP) provide an opportunity to detect undiagnosed glaucoma. Our aim was to find out which retinal image indicators are most suitable for referring DRSP patients for glaucoma assessment and to determine the glaucoma detection potential of Slovenian DRSP. Materials and Methods: We reviewed retinal images of patients from the DRSP at the University Medical Centre Ljubljana (November 2019-January 2020, May-August 2020). Patients with at least one indicator and some randomly selected patients without indicators were invited for an eye examination. Suspect glaucoma and glaucoma patients were considered accurately referred. Logistic regression (LOGIT) with patients as statistical units and generalised estimating equation with logistic regression (GEE) with eyes as statistical units were used to determine the referral accuracy of indicators. Results: Of the 2230 patients reviewed, 209 patients (10.1%) had at least one indicator on a retinal image of either one eye or both eyes. A total of 149 (129 with at least one indicator and 20 without) attended the eye exam. Seventy-nine (53.0%) were glaucoma negative, 54 (36.2%) suspect glaucoma, and 16 (10.7%) glaucoma positive. Seven glaucoma patients were newly detected. Neuroretinal rim notch predicted glaucoma in all cases. The cup-to-disc ratio was the most important indicator for accurate referral (odds ratio 7.59 (95% CI 3.98-14.47; p < 0.001) and remained statistically significant multivariably. Family history of glaucoma also showed an impact (odds ratio 3.06 (95% CI 1.02-9.19; p = 0.046) but remained statistically significant only in the LOGIT multivariable model. Other indicators and confounders were not statistically significant in the multivariable models. Conclusions: Our results suggest that the neuroretinal rim notch and cup-to-disc ratio are the most important for accurate glaucoma referral from retinal images in DRSP. Approximately half of the glaucoma cases in DRSPs may be undiagnosed.

The Effect of Myopia on Vessel Density in Glaucomatous Patients by Optical Coherence Tomography Angiography.

Glaucoma is a degenerative optic neuropathy that causes anatomical and functional visual impairment. This investigation's primary goal was to perform a qualitative and quantitative assessment of macular and peripapillary vessels to detect the impairment of blood flow in glaucomatous patients with or without myopia which can affect the prognosis of glaucoma and visual field. This prospective, cross-sectional, observational research was performed for glaucomatous patients with and without myopia who attend the outpatient clinic at the ophthalmology department, at Benha University. The study was conducted on 50 subjects with glaucomatous eyes, divided into two groups: the first group consisted of (25 subjects) of glaucoma with myopia and the second group (25 subjects) of glaucoma with the same severity of mean deviation in the visual field of group 1 without myopia, using OCTA to measure retinal vessel density (superficial vessel density) and OCT thickness ILM-RPE, RNFL thickness, GCL and small vessel density (RADIAL PERI PAPILLARY PLEXUS). Regarding demographic data of myopia in the studied eyes, there were (9) 18% with low myopia with no significance, (32) 64% with moderate myopia, and (9) 18% with high myopia, with open-angle glaucoma patients showed a highly significant decline in total retinal nerve fiber layer thickness, superior-nasal RNFL thickness, Inferior-nasal RNFL thickness, superior-temporal RNFL and inferior-temporal RNFL thickness compared to open-angle glaucoma patients without myopia. Our results show that microvascular attenuation occurs more significantly in OAG than in myopia. When both myopia and OAG are present, there is a higher reduction in microvascular attenuation than with either disease alone. The development and progression of glaucoma in individuals with high myopia are more aggressive than in low or non-myopia, so by using OCTA detection of early microvascular changes in high myopia, individuals help the early detection and management of glaucoma.

AlterNet-K: a small and compact model for the detection of glaucoma.

Glaucoma is one of the leading causes of permanent blindness in the world. It is caused due to an increase in the intraocular pressure within the eye that harms the optic nerve. People suffering from Glaucoma often do not notice any changes in their vision in the early stages. However, as it progresses, Glaucoma usually leads to vision loss that is irreversible in many cases. Thus, early diagnosis of this eye disease is of critical importance. The fundus image is one of the most used diagnostic tools for glaucoma detection. However, drawing accurate insights from these images requires them to be manually analyzed by medical experts, which is a time-consuming process. In this work, we propose a parameter-efficient AlterNet-K model based on an alternating design pattern, which combines ResNets and multi-head self-attention (MSA) to leverage their complementary properties to improve the generalizability of the overall model. The model was trained on the Rotterdam EyePACS AIROGS dataset, comprising 113,893 colour fundus images from 60,357 subjects. The AlterNet-K model outperformed transformer models such as ViT, DeiT-S, and Swin transformer, standard DCNN models including ResNet, EfficientNet, MobileNet and VGG with an accuracy of 0.916, AUROC of 0.968 and F1 score of 0.915. The results indicate that smaller CNN models combined with self-attention mechanisms can achieve high classification accuracies. Small and compact Resnet models combined with MSA outperform their larger counterparts. The models in this work can be extended to handle classification tasks in other medical imaging domains.

An automated classification framework for glaucoma detection in fundus images using ensemble of dynamic selection methods

Glaucoma is an optic neuropathy, which leads to vision loss and is irreversible due to damage in the optic nerve head mainly caused by increased intra-ocular pressure. Retinal fundus photography facilitates ophthalmologist in detection of glaucoma but is subjective to human intervention and is time-consuming. Computational methods such as image processing and machine learning classifiers can aid in computer-based glaucoma detection which helps in mass screening of glaucoma. In this context, the proposed method develops an automated glaucoma detection system, in the following steps: (i) pre-processing by segmenting the blood vessels using directional filter; (ii) segmenting the region of interest by using statistical features; (iii) extracting the clinical and texture-based features; and (iv) developing ensemble of classifier models using dynamic selection techniques. The proposed method is evaluated on two publically available datasets and 300 fundus images collected from a hospital. The best results are obtained using ensemble of random forest using META-DES dynamic ensemble selection technique, and the average specificity, sensitivity and accuracy for glaucoma detection on hospital dataset are 100%, respectively. For RIM-ONE dataset, the average specificity, sensitivity and accuracy for glaucoma detection are 100%, 93.85% and 97.86%, respectively. For Drishti dataset, the average specificity, sensitivity and accuracy for glaucoma detection are 90%, 100% and 97%, respectively. The quantitative results and comparative study indicate the ability of the developed method, and thus, it can be deployed in mass screening and also as a second opinion in decision making by the ophthalmologist for glaucoma detection.

Optical Coherence Tomography and Optical Coherence Tomography Angiography: Essential Tools for Detecting Glaucoma and Disease Progression.

Early diagnosis and detection of disease progression are critical to successful therapeutic intervention in glaucoma, the leading cause of irreversible blindness worldwide. Optical coherence tomography (OCT) is a non-invasive imaging technique that allows objective quantification in vivo of key glaucomatous structural changes in the retina and the optic nerve head (ONH). Advances in OCT technology have increased the scan speed and enhanced image quality, contributing to early glaucoma diagnosis and monitoring, as well as the visualization of critically important structures deep within the ONH, such as the lamina cribrosa. OCT angiography (OCTA) is a dye-free technique for noninvasively assessing ocular microvasculature, including capillaries within each plexus serving the macula, peripapillary retina and ONH regions, as well as the deeper vessels of the choroid. This layer-specific assessment of the microvasculature has provided evidence that retinal and choroidal vascular impairments can occur during early stages of glaucoma, suggesting that OCTA-derived measurements could be used as biomarkers for enhancing detection of glaucoma and its progression, as well as to reveal novel insights about pathophysiology. Moreover, these innovations have demonstrated that damage to the macula, a critical region for the vision-related quality of life, can be observed in the early stages of glaucomatous eyes, leading to a paradigm shift in glaucoma monitoring. Other advances in software and hardware, such as artificial intelligence-based algorithms, adaptive optics, and visible-light OCT, may further benefit clinical management of glaucoma in the future. This article reviews the utility of OCT and OCTA for glaucoma diagnosis and disease progression detection, emphasizes the importance of detecting macula damage in glaucoma, and highlights the future perspective of OCT and OCTA. We conclude that the OCT and OCTA are essential glaucoma detection and monitoring tools, leading to clinical and economic benefits for patients and society.