Detection Of Glaucoma Research Articles

Reduced contrast sensitivity, pattern electroretinogram ratio, and diminished a-wave amplitude in patients with major depressive disorder.

The electroretinogram (ERG), a non-invasive electrophysiological tool used in ophthalmology, is increasingly applied to investigate neural correlates of depression. The present study aimed to reconsider previous findings in major depressive disorder (MDD) reporting (1) a diminished contrast sensitivity and (2) a reduced patten ERG (PERG) amplitude ratio, and additionally, to assess (3) the photopic negative response (PhNR) from the flash ERG (fERG), with the RETeval® device, a more practical option for clinical routine use. We examined 30 patients with a MDD and 42 healthy controls (HC), assessing individual contrast sensitivity thresholds with an optotype-based contrast test. Moreover, we compared the PERG ratio, an established method for early glaucoma detection, between both groups. The handheld ERG device was used to measure amplitudes and peak times of the fERG components including a-wave, b-wave and PhNR in both MDD patients and HCs. MDD patients exhibited diminished contrast sensitivity together with a reduced PERG ratio, compared to HC. With the handheld ERG device, we found reduced a-wave amplitudes in MDD, whereas no significant differences were observed in the fERG b-wave or PhNR between patients and controls. The reduced contrast sensitivity and PERG ratio in MDD patients supports the hypothesis that depression is associated with altered visual processing. The findings underscore the PERG's potential as a possible objective marker for depression. The reduced a-wave amplitude recorded with the RETeval® system in MDD patients might open new avenues for using handheld ERG devices as simplified approaches for advancing depression research compared to the PERG.

Fundus- Based Glaucoma Detection- Machine Learning A Design Approach

Glaucoma is a leading cause of irreversible blindness worldwide, characterized by the progressive degeneration of the optic nerve, often associated with increased intraocular pressure. This paper proposes a machine learning-based design approach for the detection of glaucoma using fundus images. Our approach leverages advanced image processing techniques and machine learning algorithms to identify glaucomatous features in fundus images. The system comprises several key components: pre-processing of fundus images to enhance quality, feature extraction to identify relevant glaucomatous markers, and classification using machine learning models to differentiate between healthy and glaucomatous eyes. The proposed model achieved high accuracy, sensitivity, and specificity in detecting glaucoma, demonstrating its potential as a reliable and non-invasive diagnostic tool. Our findings highlight the efficacy of machine learning in medical imaging applications and underscore the importance of automated systems in assisting ophthalmologists in early glaucoma detection

Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection

Glaucoma is a progressive neurodegenerative disease characterized by the gradual degeneration of retinal ganglion cells, leading to irreversible blindness worldwide. Therefore, timely and accurate diagnosis of glaucoma is crucial, enabling early intervention and facilitating effective disease management to mitigate further vision deterioration. The advent of optical coherence tomography (OCT) has marked a transformative era in ophthalmology, offering detailed visualization of the macula and optic nerve head (ONH) regions. In recent years, both 2D and 3D convolutional neural network (CNN) algorithms have been applied to OCT image analysis. While 2D CNNs rely on post-prediction aggregation of all B-scans within OCT volumes, 3D CNNs allow for direct glaucoma prediction from the OCT data. However, in the absence of extensively pre-trained 3D models, the comparative efficacy of 2D and 3D-CNN algorithms in detecting glaucoma from volumetric OCT images remains unclear. Therefore, this study explores the efficacy of glaucoma detection through volumetric OCT images using select state-of-the-art (SOTA) 2D-CNN models, 3D adaptations of these 2D-CNN models with specific weight transfer techniques, and a custom 5-layer 3D-CNN-Encoder algorithm. The performance across two distinct datasets is evaluated, each focusing on the macula and the ONH, to provide a comprehensive understanding of the models’ capabilities in identifying glaucoma. Our findings demonstrate that the 2D-CNN algorithm consistently provided robust results compared to their 3D counterparts tested in this study for glaucoma detection, achieving AUC values of 0.960 and 0.943 for the macular and ONH OCT test images, respectively. Given the scarcity of pre-trained 3D models trained on extensive datasets, this comparative analysis underscores the overall utility of 2D and 3D-CNN algorithms in advancing glaucoma diagnostic systems in ophthalmology and highlights the potential of 2D algorithms for volumetric OCT image-based glaucoma detection.

Early detection of glaucoma: feature visualization with a deep convolutional network

ABSTRACT Computer-aided diagnosis of ocular disorders like glaucoma can be effectively performed with retinal fundus images. Since the advent of machine learning and later deep learning techniques in medical image analysis, research focusing on automated and early detection of glaucoma has gained prominence. In this paper, we show how a deep Convolutional Neural Network model can be used in visualising the features related to Glaucoma that are consistent with (a subset of) those used by ophthalmologists for diagnosis. However, since the doctors do not necessarily depend entirely on these features but back them up with other important measurements such as visual field testing (HVF), any deep neural network such as CNN may not necessarily be expected to yield 100% accuracy. Further, other important parameters such as Intraocular Pressure (IOP), which may be built into a deep learning model, may not necessarily correlate well with the presence of Glaucoma. In this work, a deep CNN model has been trained and tested on a large number of high-quality fundus images, both normal and glaucomatous. We compare the results with transfer learning models such as VGG16, ResNet50, and MobileNetV2. On an average, we obtained an accuracy of 93.75% in identifying glaucoma, focusing only on the features of the fundus image.

Glaucoma Detection using Fundus Images by Extracting Localized Disc Features

Glaucoma is one of the leading causes of blindness worldwide. It occurs due to high pressure in the eyes and other factors such as family history, age, ethnicity, etc. It damages the optic nervous system which is irreversible damage. That's why regular screening for glaucoma is crucial and recommended. Researchers are continuously searching for better methods to identify glaucoma at early stages before it becomes worse and incurable. Significant work has been conducted on it, but there is still room for improvement. The main goal of this study is to propose a reliable system for glaucoma detection that considers the key factors contributing to glaucoma development, in accordance with the decisions made by clinical experts. In this work, the U-net model is used with EfficentNetb3 as a backbone model for optic disc and optical cup segmentation. In addition, a general deep learning model that has 1D convolution layers and other basic layers is used for glaucoma classification. Features extracted from optical disc and optical cup are used to train the deep learning model and overall 95% classification accuracy and 0.92 AUC are achieved for glaucoma classification on the RIGA dataset.

Fluorescent Quantum Dots Based Lateral Flow Assay for Rapid Quantitative Detection of Ciliary Neurotrophic Factor in Glaucoma

AbstractEarly and accurate diagnosis of glaucoma is crucial to prevent the progressive deterioration that leads to irreversible vision loss. It is imperative to develop an effective screening tool for glaucoma. Ciliary neurotrophic factor (CNTF) is a tear biomarker implicated in glaucoma pathogenesis. Lateral flow assay (LFA) provides an ideal platform for detection of glaucoma in tear fluid. A quantum dot‐based fluorescence LFA, integrated with a 3D printed readout box, is developed for fast (30 min), sensitive, and quantitative CNTF detection in tears. A standard curve is firstly generated for the quantitative detection of CNTF. The limit of detection (LOD) of the obtained LFA strip at 6.45 pg·mL−1 is comparable to that of the enzyme‐linked immunosorbent assay (ELISA) at 6.42 pg·mL−1. This enables the identification of low CNTF levels (25.7 ± 14.9 pg·mL−1) reported in tear fluid from glaucoma patients. This LFA is found to be highly selective for CNTF and maintained consistent results in different pH condition. The strip remaines stablewhen stored in the darkat room temperature. A smartphone app is developed to simplify analysis and enable prompt and easily obtainable results. This method shows great potential to be a powerful tool for point‐of‐care glaucoma screening.

Diagnostic Accuracy of Optic Nerve Head and Macula OCT Parameters for Detecting Glaucoma in Eyes With and Without High Axial Myopia

PurposeTo characterize structural differences and assess the diagnostic accuracy of optic nerve head (ONH) and macula optical coherence tomography (OCT) parameters to detect glaucoma in eyes with and without high axial myopia. DesignCross-sectional study Methods368 glaucoma and 411 healthy eyes with no axial myopia, 393 glaucoma and 271 healthy eyes with mild axial myopia and 124 glaucoma and 85 healthy eyes with high axial myopia were included. Global and sectoral peripapillary retinal nerve fiber layer thickness (pRNFLT), Bruch's membrane opening minimum rim width (BMO-MRW), ganglion cell inner plexiform layer thickness (GCIPLT), and macula RNFLT (mRNFLT) were compared and the diagnostic accuracy for glaucoma detection was evaluated using adjusted area under the receiver operating characteristic curve (AUC). ResultsDiagnostic accuracy for ONH and macula parameters to detect glaucoma was generally high and differed by myopia group. For ONH parameters the diagnostic accuracy was highest for global (AUC=0.95) and inferotemporal (AUC=0.91) pRNFLT for high myopes and global BMO-MRW for non-myopes (AUC=1.0) and mild myopes (AUC=0.97). For macula parameters, the diagnostic accuracy was higher in high myopes with 6 of the 11 GCIPLT global/sectors having adjusted AUCs > 0.90 compared to non-high myopes with no AUCs > 0.90. In all myopia groups, mRNFLT had lower AUCs than GCIPLT. ConclusionsThe diagnostic accuracy for pRNFL and GCIPL was high for high axial myopic eyes and shows promise for glaucoma detection in high myopes. Further analysis is needed to determine whether the high diagnostic accuracy can be confirmed in other populations.

Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography.

To assess the diagnostic performance and structure-function association of retinal retardance (RR), a customized metric measured by a prototype polarization-sensitive optical coherence tomography (PS-OCT), across various stages of glaucoma. This cross-sectional pilot study analyzed 170eyes from 49 healthy individuals and 68 patients with glaucoma. The patients underwent PS-OCT imaging and conventional spectral-domain optical coherence tomography (SD-OCT), as well as visual field (VF) tests. Parameters including RR and retinal nerve fiber layer thickness (RNFLT) were extracted from identical circumpapillary regions of the fundus. Glaucomatous eyes were categorized into early, moderate, or severe stages based on VF mean deviation (MD). The diagnostic performance of RR and RNFLT in discriminating glaucoma from controls was assessed using receiver operating characteristic (ROC) curves. Correlations among VF-MD, RR, and RNFLT were evaluated and compared within different groups of disease severity. The diagnostic performance of both RR and RNFLT was comparable for glaucoma detection (RR AUC = 0.98, RNFLT AUC = 0.97; P = 0.553). RR showed better structure-function association with VF-MD than RNFLT (RR VF-MD = 0.68, RNFLT VF-MD = 0.58; z = 1.99; P = 0.047) in glaucoma cases, especially in severe glaucoma, where the correlation between VF-MD and RR (r = 0.73) was significantly stronger than with RNFLT (r = 0.43, z = 1.96, P = 0.050). In eyes with early and moderate glaucoma, the structure-function association was similar when using RNFLT and RR. RR and RNFLT have similar performance in glaucoma diagnosis. However, in patients with glaucoma especially severe glaucoma, RR showed a stronger correlation with VF test results. Further research is needed to validate RR as an indicator for severe glaucoma evaluation and to explore the benefits of using PS-OCT in clinical practice. We demonstrated that PS-OCT has the potential to evaluate the status of RNFL structural damage in eyes with severe glaucoma, which is currently challenging in clinics.

Increased late-onset glaucoma risk following vitrectomy for macular pucker or hole.

The long-term risk of developing glaucoma after vitrectomy remains uncertain. This retrospective population-based cohort study aimed to explore this risk following vitrectomy for macular pucker or hole. Utilizing Taiwan's National Health Insurance Research Database (NHIRD), we included patients who were older than 18 years and had undergone vitrectomy surgery between 2011 and 2019. Exclusions were made for patients with prior diagnoses of glaucoma, congenital or secondary glaucoma, as well as those who had received previous vitreoretinal treatments or had undergone multiple vitrectomies. After an average follow-up period of 51 and 53 months respectively for the vitrectomized and non-vitrectomized group, our results showed a relative risk of 1.71 for glaucoma development in the vitrectomized group. Higher adjusted hazard ratios were also observed for open-angle glaucoma and normal tension glaucoma. Increased risks were associated with male sex, obstructive sleep apnoea, and migraine. In the subgroup analysis, phakic eyes at baseline and those who had undergone cataract surgery post-vitrectomy were associated with a lower risk of glaucoma development during follow-up. Among all glaucoma events, pseudophakic status at baseline had the shortest interval to glaucoma development following vitrectomy. These findings underscore the potential relationship between vitrectomy and glaucoma onset, emphasizing the need for vigilant monitoring and early detection of glaucoma in post-vitrectomy patients.

Glaucoma detection using non-perfused areas in OCTA

Multiple ophthalmic diseases lead to decreased capillary perfusion that can be visualized using optical coherence tomography angiography images. To quantify the decrease in perfusion, past studies have often used the vessel density, which is the percentage of vessel pixels in the image. However, this method is often not sensitive enough to detect subtle changes in early pathology. More recent methods are based on quantifying non-perfused or intercapillary areas between the vessels. These methods rely upon the accuracy of vessel segmentation, which is a challenging task and therefore a limiting factor for reliability. Intercapillary areas computed from perfusion-distance measures are less sensitive to errors in the vessel segmentation since the distance to the next vessel is only slightly changing if gaps are present in the segmentation. We present a novel method for distinguishing between glaucoma patients and healthy controls based on features computed from the probability density function of these perfusion-distance areas. The proposed approach is evaluated on different capillary plexuses and outperforms previously proposed methods that use handcrafted features for classification. Moreover the results of the proposed method are in the same range as the ones of convolutional neural networks trained on the raw input images and is therefore a computationally efficient, simple to implement and explainable alternative to deep learning-based approaches.

Optic Cup and Optic Disc Segmentation Based on improved TransUnet

Glaucoma ranks as the second leading cause of blindness globally, surpassed only by cataracts. It inflicts irreversible harm to the optic nerve, and once vision is lost, restoration is unattainable for life[1]. Therefore, early detection of glaucoma is imperative.The cup-to-disc ratio serves as a primary diagnostic tool for identifying glaucoma. Generally, an excessively large cup-to-disc ratio in fundus photographs strongly suggests glaucoma. However, human error in diagnosing fundus photographs by clinicians is prevalent, leading to time-consuming, labor-intensive, expensive, and potentially inaccurate diagnoses during extensive screenings.To combat this challenge, an enhanced TransUnet-based method is proposed for optic cup and optic disc segmentation, aiding clinicians in large-scale glaucoma screenings. The model incorporates the Focus structure and CBAM structure. The Focus structure addresses information loss during single downsampling of images, preserving more data without changing image dimensions. Meanwhile, the CBAM structure integrates channel and spatial attention, enhancing the model's ability to extract features.On the ORIGA-650 dataset, this enhanced method achieved a mean Dice coefficient of 0.984 for the disc and 0.947 for the cup, along with a mean IOU value of 0.968 for the disc and 0.902 for the cup. Compared to alternative algorithms, the segmentation results exhibit superior accuracy, showcasing the efficacy of our proposed model.

A hybrid framework for glaucoma detection through federated machine learning and deep learning models

BackgroundGlaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts.MethodDeveloping a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment.ResultThe hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis.ConclusionIn summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology’s potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare.

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

CDAM-Net: Channel shuffle dual attention based multi-scale CNN for efficient glaucoma detection using fundus images

Glaucoma is a typical eye disorder that induces damage to the optic nerve due to increased intraocular pressure. It ultimately leads to partial or complete blindness without clinical reversal. Hence, it is of utmost importance to screen and detect glaucoma at an early stage. Most of the earlier glaucoma diagnosis methods rely on manual feature engineering, which is time-consuming and requires domain experts. Although recent methods, particularly, convolutional neural networks (CNNs) facilitate learning high-level feature representations from fundus images, they need extensive parameters and pose overfitting issues due to insufficient training samples. Further, conventional CNNs often ignore the minute changes in the lesion region. To overcome these issues, we propose a lightweight multi-scale CNN architecture called as CDAM-Net for effective glaucoma identification from retinal fundus images. Additionally, we introduce an attention module called channel shuffle dual attention (CSDA), comprising of a channel attention block, a spatial attention block and a channel shuffle unit, to focus on important regions in the fundus images, thereby extracting class-specific features. The CDAM-Net mainly consists of multi-scale feature representation (MFR) blocks that enable the extraction of multi-scale features from fundus images. Each MFR block is followed by a CSDA module, which further helps enrich the feature representation. The CDAM-Net is evaluated on a retinal fundus image (RFI) dataset containing 1426 fundus images (837 glaucoma and 589 normal), and the results indicate that CDAM-Net yields promising classification performance compared to existing techniques. Also, ablation studies are carried out to test the effectiveness of each component of the CDAM-Net.

AES-Net: An adapter and enhanced self-attention guided network for multi-stage glaucoma classification using fundus images

Glaucoma is a progressive eye condition that can lead to permanent vision loss. Therefore, on-time detection of glaucoma is critical for making an effective treatment plan. In recent years, enormous attempts have been made to develop automated glaucoma classification systems using CNNs through images. In contrast, limited methods have been proposed for diagnosing different glaucoma stages. It is mainly owing to the lack of large publicly available labeled datasets. Also, fundus images exhibit a high inter-stage resemblance, redundant features and minute size variations of lesions, making the conventional CNNs difficult to classify multiple stages of glaucoma accurately. To address these challenges, this paper proposes a novel adapter and enhanced self-attention based CNN framework named AES-Net for effective classification of glaucoma stages. In particular, we propose a spatial adapter module on top of the backbone network for learning better feature representations and an enhanced self-attention module (ESAM) to capture global feature correlations among the relevant channels and spatial positions. The ESAM assists in capturing stage-specific and detailed-lesion features from the fundus images. Extensive experiments on two multi-stage glaucoma datasets indicate that our AES-Net surpasses CNN-based existing approaches. The Grad-CAM++ visualization maps further confirm the effectiveness of our AES-Net.

Migraine headache and other risk factors associated with glaucoma among the adult population living in Armenia: a case-control study.

This study aimed to assess the association between migraine headache and glaucoma among the adult population living in Armenia. This case-control study recruited 145 cases with glaucoma and 250 controls without glaucoma and other ocular disorders except refractive error from Optomed Canada Diagnostic Eye Center in Armenia. A structured questionnaire contained questions on socio-demographics, family history of glaucoma and stroke, ocular health, smoking, migraine, and obstructive sleep apnea. The Migraine Screening Questionnaire assessed possible migraine and the Berlin Questionnaire measured obstructive sleep apnea. The mean ages of cases and controls were 63.3 (SD = 12.3) and 39.5 (SD = 13.5), respectively. Females comprised 62.8% of cases and 69.1% of controls. A total of 17.8% of cases and 19.0% of controls had possible migraine. In the adjusted analysis older age (OR 1.17; 95% CI 1.12; 1.23), average/lower than average socio-economic status (OR 5.27; 95% CI 1.30; 21.3), and family history of glaucoma (OR 4.25; 95% CI 1.51; 11.9) were associated with high-tension glaucoma. Timely case detection of glaucoma among those with average/low socio-economic status and those with family history of glaucoma could prevent further progression of the disease. Further studies to explore the relationship between migraine headache and specific types of glaucoma may be worthwhile.

Development and evaluation of Order of Magnitude (OM): a virtual reality-based visual field analyzer for glaucoma detection.

This study introduces the Order of Magnitude (OM), a cost-effective, indigenous, virtual reality-based visual field analyzer designed for detecting glaucomatous visual field loss. The OM test employs a two-step supra-thresholding algorithm utilizing stimuli of 0.43°diameter (equivalent to Goldmann size III) at low and high thresholds. A comparative analysis was conducted against the Humphrey visual field (HVF) test, considered the gold standard in clinical practice. Participants, including those with glaucoma and normal individuals, underwent comprehensive eye examinations alongside the OM and HVF tests between April and October 2019. Diagnostic sensitivity and specificity of the OM test were assessed against clinical diagnoses made by specialists. We studied 157 eyes (74 glaucomatous, 83 control) of 152 participants. Results demonstrated a high level of reliability for both OM and HVF tests, with no significant difference observed (P = 0.19, Chi-square test). The sensitivity and specificity of the OM test were found to be 93% (95% CI 86-100%) and 83% (95% CI 72.4-93%), respectively, while the HVF test showed sensitivity and specificity of 98% (95% CI 93.9-100%) and 83% (95% CI 73.9-92.8%), respectively. These findings suggest that the OM test is non-inferior to the reference standard HVF test in identifying glaucomatous visual field loss.

DynaGlaucoDetect: Leveraging Dyna-Q learning for glaucoma detection

Glaucoma is an eye disease that requires early detection and proper diagnosis for timely intervention and treatment which can help slow down further progression and to manage intraocular pressure. This paper aims to address the problem by proposing a novel approach that combines a model-based Reinforcement Learning (RL) approach, called DynaGlaucoDetect, with ocular gaze data. By leveraging the RL algorithms to simulate and predict the dynamics of glaucoma, a model-based approach can improve the accuracy and efficiency of glaucoma detection by enabling better preservation of visual health. The RL agent is trained using real experiences and synthetic experiences which are generated using the model-based algorithm Dyna-Q. Two different Q-table generation methods have been discussed: the Direct Synthesis Method (DSM) and the Indirect Synthesis Method (IdSM). The presence of glaucoma has been detected by comparing the reward score a patient obtains with the threshold values obtained through the performed experimentation. The scores obtained using DSM and IdSM have been compared to understand the learning of the agent in both cases. Finally, hyperparameter tuning has been performed to identify the best set of hyperparameters.

Barriers and Potential Solutions to Glaucoma Screening in the Developing World: A Review.

Glaucoma is a leading public health concern globally and its detection and management are way more complex and challenging in the developing world. This review article discusses barriers to glaucoma screening in developing countries from the perspective of different key stakeholders and proposes solutions. A literature search was carried out in the electronic catalogs of PubMed, Medline, and Cochrane database of systematic reviews to find studies that focused on barriers and enablers to glaucoma screening. The authors' interpretations were tabulated as descriptive and qualitative data and presented concisely from the point of view of key stakeholders such as the patients and their relatives, care providers, and system/governing bodies. Key barriers to glaucoma care identified are lack of awareness, poor accessibility to ophthalmic centers, inadequately trained human resources, unsatisfactory infrastructure, and nonavailability of financially viable screening programs. Educating care providers, as well as the public, providing care closer to where people live, and developing cost-effective screening strategies are needed to ensure proper identification of glaucoma patients in developing countries. The logistics of glaucoma detection and management are complex. Hence, glaucoma detection programs should be implemented only when facilities for glaucoma management are in place. Understanding the importance of glaucoma screening and its future implications, addressing the various roadblocks, empowering and efficiently implementing the existing strategies, and incorporating novel ones using Artificial Intelligence (AI) and deep learning (DL) will help in establishing a robust glaucoma screening program in developing countries.

A deep learning model based glaucoma detection using retinal images

The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the source retinal images into either healthy or Glaucoma through the detection and segmentations of optic disc (OD) and optic cup (OC) regions in retinal pictures. The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC regions are detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region are classified and trained by the suggested AlexNet deep leaning model. This model classifies the source retinal image into either healthy or Glaucoma. Finally, performance measures have been estimated in relation to ground truth pictures in regards to accuracy, specificity and sensitivity. These performance measures are contrasted with the other previous Glaucoma detection techniques on publicly accessible retinal image datasets HRF and RIGA. The suggested technique as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. AIM: Segmenting the OD and OC areas and classifying the source retinal picture as either healthy or glaucoma-affected. METHODS: The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC region is detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region classified are and trained by the suggested AlexNet deep leaning model. RESULTS: The suggested method as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. CONCLUSION: This article proposes the modified AlexNet deep learning models for the detections of Glaucoma utilizing retinal images. The OD region is detected using circulatory filter and OC region is detected using k-means classification algorithm. The detected OD and OC regions are utilized to classify the retinal images into either healthy or Glaucoma using the suggested AlexNet model. The proposed method obtains 100% Sey, 93.7% Spy and 96.6% CA on HRF dataset retinal images. The proposed AlexNet method obtains 97.7% Sey, 98% Spy and 97.8% CA on RIGA dataset retinal images. The proposed method stated in this article achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset.