Glaucoma Assessment Research Articles

Depth-resolved extraction of optical attenuation for glaucoma assessment in clinical settings: a pilot study.

We assessed the ability of the optical attenuation coefficient (AC) to detect early-stage glaucoma with two AC estimation algorithms: retinal layer intensity ratio (LIR) and depth-resolved confocal (DRC). We also introduced new depth-dependent AC parameters for retinal nerve fiber layer assessment. Optical coherence tomography B-scans were collected from 44 eyes of age-similar participants with eye health ranging from healthy to severe glaucoma, including glaucoma suspect patients. Mean AC values estimated from the DRC method are comparable to ratio-extracted values (p > 0.5 for all study groups), and the depth-dependent ACDRC parameters enhance the utility of the AC for detection of early-stage glaucoma.

Comparison of Central Corneal Thickness Measurements by Ultrasound Pachymetry and Non-Contact Specular Microscopy in Normal Eyes

Central corneal thickness measurement plays a crucial role in the assessment and treatment of glaucoma and several corneal diseases in addition to its importance in refractive surgery management. There are various methods for central corneal thickness measurement, which differ in their operating principles and technology beside the difference in their advantages and limitation. This study aims to compare central corneal thickness (CCT) values of healthy eyes measured by two common devices: ultrasound pachymetry (UP) and non-contact specular microscopy (NCSM) and to determine the average difference between the two measurements. The study was conducted in the ophthalmology outpatient clinic in Imam Al-Sadiq Hospital- Hilla city and included 99 healthy individuals (198 eyes) of both genders. Results indicated that NCSM tends to give thinner CCT values compared to UP while providing quicker, safer and more comfortable evaluation for corneal thickness in addition to corneal endothelial cell density.

Automated glaucoma assessment from color fundus images using structural and texture features

Glaucoma is an eye disease that damages optic nerves in eye leading to loss of vision. It is recognized to be the one of the major cause of blindness across the world. The detection of glaucoma in its early stage followed by treatment is the only way forward because damage done by the disease is irreversible. Hence there is a requirement of a large scale glaucoma screening. Manual screening of glaucoma at large scale is quite a challenging task due to lack of skilled manpower in ophthalmology. To address the above issue, many works have been proposed towards automating glaucoma detection suitable for large scale screening. On similar line, we in this proposed work present a novel method for automated glaucoma assessment from color fundus images (CFI) using structural features and texture features. Structural features such as cup to disk ratio (CDR), rim to disc ratio (RDR) and texture features extracted through the Grey-Level Co occurrence Matrix (GLCM) and entropy of images, histogram of gradient (HOG) features are used for glaucoma assessment. The classifiers used are Linear Discriminant Analysis (LDA), Random forest, Support Vector Machine (SVM) and Naïve Bayesian. The results obtained by the proposed work yield an overall efficiency of over 89%.

PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment

Glaucoma is one of the ophthalmological diseases that frequently causes loss of vision in today’s society. Previous studies assess which anatomical parameters of the optic nerve can be predictive of glaucomatous damage, but to date there is no test that by itself has sufficient sensitivity and specificity to diagnose this disease. This work provides a public dataset with medical data and fundus images of both eyes of the same patient. Segmentations of the cup and optic disc, as well as the labeling of the patients based on the evaluation of clinical data are also provided. The dataset has been tested with a neural network to classify healthy and glaucoma patients. Specifically, the ResNet-50 has been used as the basis to classify patients using information from each eye independently as well as using the joint information from both eyes of each patient. Results provide the baseline metrics, with the aim of promoting research in the early detection of glaucoma based on the joint analysis of both eyes of the same patient.

Glaucoma diagnosis using multi-feature analysis and a deep learning technique

In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.

An Automated Framework for Screening of Glaucoma using Cup-to-Disc Ratio and ISNT Rule with a Support Vector Machine

Glaucoma is a common eye disease that damages an optic nerve due to abnormally high pressure inside the eye. Glaucoma can cause visual impairments and eventually lead to blindness. There is no appropriate treatment to prevent blindness when the optic nerve is damaged. Therefore, an early diagnosis is important to prevent vision loss from glaucoma. An automated framework for glaucoma screening from fundus images is advantageous. It can facilitate the ophthalmologist in the diagnosis and prevent blindness. Many glaucoma screening algorithms have been developed based on a clinical indicator, the cup-to-disc ratio (CDR). However, these algorithms have some limitations for myopia and genetically large optic cup eyes. Therefore, this paper proposes a framework for glaucoma screening that can be applied even in myopia. The 2 clinical indicators, cup-to-disc ratio (CDR) and neuroretinal rim area rule (inferior > superior > nasal > temporal (ISNT)), are applied in the proposed screening algorithm for accurate glaucoma assessment. Moreover, the automatic classification of glaucoma or non-glaucoma from fundus images is performed by a support vector machine (SVM). Therefore, the experimental results show that the proposed screening algorithm can accurately classify glaucoma to normal eyes or myopic eyes.
 HIGHLIGHTS
 
 A framework for glaucoma screening is proposed to classify myopia and genetically large optic cup eyes
 Two clinical indicators, cup-to-disc ratio (CDR) and neuroretinal rim area rule (inferior > superior > nasal > temporal (ISNT)), are applied in the proposed screening algorithm for accurate glaucoma assessment
 Automatic classification of glaucoma or non-glaucoma from fundus images is performed by a support vector machine (SVM)
 The proposed framework can provide less time consuming, cost-effective and physician independent system for glaucoma screening
 
 GRAPHICAL ABSTRACT

A Structure-Related Fine-Grained Deep Learning System With Diversity Data for Universal Glaucoma Visual Field Grading.

PurposeGlaucoma is the main cause of irreversible blindness worldwide. However, the diagnosis and treatment of glaucoma remain difficult because of the lack of an effective glaucoma grading measure. In this study, we aimed to propose an artificial intelligence system to provide adequate assessment of glaucoma patients.MethodsA total of 16,356 visual fields (VFs) measured by Octopus perimeters and Humphrey Field Analyzer (HFA) were collected, from three hospitals in China and the public Harvard database. We developed a fine-grained grading deep learning system, named FGGDL, to evaluate the VF loss, compared to ophthalmologists. Subsequently, we discuss the relationship between structural and functional damage for the comprehensive evaluation of glaucoma level. In addition, we developed an interactive interface and performed a cross-validation study to test its auxiliary ability. The performance was valued by F1 score, overall accuracy and area under the curve (AUC).ResultsThe FGGDL achieved a high accuracy of 85 and 90%, and AUC of 0.93 and 0.90 for HFA and Octopus data, respectively. It was significantly superior (p < 0.01) to that of medical students and nearly equal (p = 0.614) to that of ophthalmic clinicians. For the cross-validation study, the diagnosis accuracy was almost improved (p < 0.05).ConclusionWe proposed a deep learning system to grade VF of glaucoma with a high detection accuracy, for effective and adequate assessment for glaucoma patients. Besides, with the convenient and credible interface, this system can promote telemedicine and be used as a self-assessment tool for patients with long-duration diseases.

Correlation Between Optical Coherence Tomography and Photopic Negative Response of Flash Electroretinography in Ganglion Cell Complex Assessment in Glaucoma Patients.

PurposeTo investigate the correlation between the photopic negative response (PhNR) of the light-adapted flash electroretinography (ERG) and measurements of standard automated perimetry (SAP) and optical coherence tomography (OCT) in assessment of retinal ganglion cells’ (RGCs) affection in glaucoma.Patients and MethodsA cross-sectional study included 40 eyes of glaucoma patients and 40 eyes of age- and gender-matched normal subjects. Participants underwent a complete ophthalmologic assessment, SAP, OCT, and light-adapted flash ERG using the extended PhNR protocol of the International Society for Clinical Electrophysiology of Vision (ISCEV). Glaucomatous eyes were divided into 3 subgroups: mild (n = 15), moderate (n = 11) and severe glaucoma (n = 14) according to the mean deviation (MD) of SAP. Measurements of SAP, OCT and ERG parameters were analyzed, and correlations between PhNR measurements and other study measurements were evaluated.ResultsPhNR amplitudes and PhNR/b-wave ratios were significantly reduced in glaucoma cases compared to healthy controls, and they showed a significant and progressive decline across the three glaucoma subgroups (P < 0.05). An exception to this is PT (b-wave peak to PhNR trough) PhNR amplitude where its reduction was statistically non-significant when comparing between controls and mild glaucoma cases (P = 0.178), and between moderate and severe glaucoma cases (P = 0.714). PhNR amplitudes and PhNR/b-wave ratios correlated significantly with SAP and OCT parameters (P < 0.05).ConclusionPhNR correlates well with SAP and OCT parameters in glaucoma assessment. PhNR could be a valuable supplementary tool for objective assessment of the RGCs’ function in glaucoma.

Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device.

Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patients earlier for a more thorough evaluation. This work explored and compared the performance of classification and segmentation methods for glaucoma screening with retinal images acquired by both retinography and mobile devices. The goal was to verify the results of these methods and see if similar results could be achieved using images captured by mobile devices. The used classification methods were the Xception, ResNet152 V2 and the Inception ResNet V2 models. The models’ activation maps were produced and analysed to support glaucoma classifier predictions. In clinical practice, glaucoma assessment is commonly based on the cup-to-disc ratio (CDR) criterion, a frequent indicator used by specialists. For this reason, additionally, the U-Net architecture was used with the Inception ResNet V2 and Inception V3 models as the backbone to segment and estimate CDR. For both tasks, the performance of the models reached close to that of state-of-the-art methods, and the classification method applied to a low-quality private dataset illustrates the advantage of using cheaper lenses.

Glaucoma Detection Using Support Vector Machine Method Based on Spectralis OCT.

Spectralis optical coherence tomography (OCT) provided more detailed parameters in the peripapillary and macular areas among the OCT machines, but it is not easy to understand the enormous information (114 features) generated from Spectralis OCT in glaucoma assessment. Machine learning methodology has been well-applied in glaucoma detection in recent years and has the ability to process a large amount of information at once. Here we aimed to analyze the diagnostic capability of Spectralis OCT parameters on glaucoma detection using Support Vector Machine (SVM) classification method in our population. Our results showed that applying all OCT features with the SVM method had good capability in the detection of glaucomatous eyes (area under curve (AUC) = 0.82), as well as discriminating normal eyes from early, moderate, or severe glaucomatous eyes (AUC = 0.78, 0.89, and 0.93, respectively). Apart from using all OCT features, the minimum rim width (MRW) may be good feature groups to discriminate early glaucomatous from normal eyes (AUC = 0.78). The combination of peripapillary and macular parameters, including MRW_temporal inferior (TI), MRW_global (G), ganglion cell layer (GCL)_outer temporal (T2), GCL_inner inferior (I1), peripapillary nerve fiber layer thickness (ppNFLT)_temporal superior (TS), and GCL_inner temporal (T1), provided better results (AUC = 0.84). This study showed promise in glaucoma management in the Taiwanese population. However, further validation study is needed to test the performance of our proposed model in the real world.

CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images

In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection avoids severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent artificial intelligence has been confirmed beneficial for glaucoma assessment. In this paper, we describe an approach to automate glaucoma diagnosis using funds images. The setup of the proposed framework is in order: The Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm is applied to decompose the Regions of Interest (ROI) to components (BIMFs+residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. Then, we fuse the features of the same ROI in a bag of features. These last very long; therefore, Principal Component Analysis (PCA) are used to reduce features dimensions. The bags of features obtained are the input parameters of the implemented classifier based on the Support Vector Machine (SVM). To train the built models, we have used two public datasets, which are ACRIMA and REFUGE. For testing our models, we have used a part of ACRIMA and REFUGE plus four other public datasets, which are RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF. The overall precision of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% is obtained on ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, by using the model trained on REFUGE. Again an accuracy of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36% is obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, using the model training on ACRIMA. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer.

The clinical diagnostic evaluation of optic neuropathies relies on the analysis of the thickness of the retinal nerve fibre layer (RNFL) by optical coherence tomography (OCT). However, false positives and false negatives in the detection of RNFL abnormalities are common. Here we show that an algorithm integrating measurements of RNFL thickness and reflectance from standard wide-field OCT scans can be used to uncover the trajectories and optical texture of individual axonal fibre bundles in the retina and to discern distinctive patterns of loss of axonal fibre bundles in glaucoma, compressive optic neuropathy, optic neuritis and non-arteritic anterior ischaemic optic neuropathy. Such optical texture analysis can detect focal RNFL defects in early optic neuropathy, as well as residual axonal fibre bundles in end-stage optic neuropathy that were indiscernible by conventional OCT analysis and by red-free RNFL photography. In a diagnostic-performance study, optical texture analysis of the RNFL outperformed conventional OCT in the detection of glaucoma, as defined by visual-field testing or red-free photography. Our findings show that optical texture analysis of the RNFL for the detection of optic neuropathies is highly sensitive and specific.

Has the use of optical coherence tomography in community optometrists’ referrals improved diagnostic accuracy for glaucoma?

AbstractPurposeIn recent years, community optometry practices have begun to use optical coherence tomography (OCT) as part of glaucoma assessment, the result of which is increasingly cited as a reason for referral into hospital glaucoma clinics. We aim to assess whether the addition of OCT scanning has improved diagnostic accuracy of glaucoma referrals from community optometry practices.MethodsA prospective observational study was conducted, which includes all glaucoma referrals from community optometrists to the Portsmouth Glaucoma Referral Refinement (GRR) scheme from July 2019 until February 2020. Patient demographics, tests performed, reasons for referral, and the final diagnosis from glaucoma clinic were collated. A positive diagnosis includes open angle glaucoma, ocular hypertension, glaucoma suspect and narrow angle glaucoma.ResultsA total of 248 eligible referrals were analysed. Overall, the positive predictive rate was 20% (n = 50), which is similar to previous audits of glaucoma referrals in Portsmouth (1). 25 subjects had an OCT scan as part of their referral, and the OCT given as a reason for referral in 24 of these cases. 25% (n = 6) resulted in a positive diagnosis; however, all these patients had at least 2 other abnormal tests at initial assessment (from IOP, disc appearance, and visual field testing) which were also reasons for referral, suggesting they would have been referred regardless of the OCT. In six cases, the only reason for referral was OCT findings, none resulted in a positive diagnosis after clinic review. The highest PPV (75%, n = 6) was achieved when all three of IOP, disc, and visual field were given as reasons for referral.ConclusionsThe use of OCT in glaucoma assessment at community optometry practices is still not very widespread. This early evidence suggests OCT performed by community optometrists does not improve diagnostic accuracy of referrals into hospital eye services.Reference1. Lockwood, A., Kirwan, J. and Ashleigh, Z., 2010. Optometrists referrals for glaucoma assessment: a prospective survey of clinical data and outcomes. Eye, 24(9), pp. 1515–1519.

Optic disc and cup segmentations for glaucoma assessment using cup-to-disc ratio

Glaucoma is a silent disease that shows symptoms when severe, leading to partial vision loss or irreversible blindness. Early screening permits treating patients in time. For glaucoma screening, retinal images are very important since they enable the observation of initial glaucoma lesions, which typically begins with the cupping formation in the optic disc (OD). In clinical settings, practical indicators such as Cup-to-Disc Ratio (CDR) are frequently used to evaluate the presence and stage of glaucoma. The ratio between the cup and the optic disc can be measured using the vertical or horizontal diameter, or the area of the two. Mass screening programs are limited by the high costs of specialised teams and equipment. Current deep learning (DL) methods can assist the glaucoma mass screening, lower the cost and allow it to be extended to larger populations. With DL methods in the OD and optic cup (OC) segmentation, is possible to evaluate the presence of glaucoma in the patient more quickly based on cupping formation in the OD, using CDR.In this work, is assessed the contribution of Multi-Class and Single-Class segmentation methods for glaucoma screening using the 3 types of CDR. U-Net architecture is trained using transfer learning models (Inception V3 and Inception ResNet V2) to segment the OD and OC and then evaluate glaucoma prediction based on different types of CDRs indicators. The models were trained and evaluated on main public known databases (REFUGE, RIM-ONE r3 and DRISHTI-GS). The segmentation of both OD and OC reach Dice over 0.8 and IoU above 0.7. The CDRs were computed to glaucoma assessment where was reach sensitivity above 0.8, specificity of 0.7, F1-Score around 0.7 and AUC above 0.85. Finally, conclusions of segmentation methods showing adequate performance to be used in practical glaucoma screening.

Machine Learning automatic assessment for glaucoma and myopia based on Corvis ST data

Glaucoma is a silent disease characterized by progressive degeneration of retinal ganglion cells and, when not detected or treated early, can lead to blindness. Computer systems have demonstrated their efficiency in the medical decision-making process and Artificial Intelligence (AI) techniques have helped advances in ophthalmology, allowing for faster and more effective detection of glaucoma. Machine learning is a very promising subfield of AI that supports research in understanding the development, progression and treatment of glaucoma, identifying new risk factors and assessing the importance of existing ones.This study aims to test and analyze the results of different models of supervised machine learning in the detection and classification of ophthalmic diseases (Glaucoma, high myopia and low myopia) based on data from Corvis ST. The most important characteristics were selected based on a variance greater than 0.02. In terms of accuracy, the models that obtained the best results were Random Forrest 0.73, Stochastic Gradient Descent (SGD) 0.75, Gradient Boosting Classifier (GBC) 0.76 and K-Nearest Neighbors 0.71. The GBC model achieved the best results in accuracy, AUC, Recall and F1Score 76.00, 52.5, 78.00, 70.2 respectively.

Automated Glaucoma Assessment from Color Fundus Images Using Structural and Texture Features

Automated Glaucoma Assessment from Color Fundus Images Using Structural and Texture Features

A Case for the Use of Artificial Intelligence in Glaucoma Assessment

We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community on Ophthalmic Imaging meeting, "The Future of Artificial Intelligence-Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways," held virtually September 3-4,2020. The Collaborative Community on Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was 1 of the first 2 such organizations officially designated by the Food and Drug Administration in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018-2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, because access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of AI technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert-level evaluation to individuals even in the most remote regions of the planet.

Comparison of intraocular pressure peak and fluctuations among Filipino patients with non-glaucomatous eyes and glaucoma suspects using water drinking test and diurnal intraocular pressure.

To compare the intraocular pressure (IOP) peaks and fluctuations using water drinking tests (WDTs) and mean diurnal IOP among Filipino patients with normal eyes and glaucoma suspects. This prospective study included normal and glaucoma suspect patients. Each patient underwent both WDT and mean diurnal examination on separate visits. For mean diurnal examination, IOP was recorded every 2h for 8h while in WDT, IOP was recorded prior to WDT, and post-WDT at 5, 15, 30, 45, and 60min. IOP peak was recorded as the highest IOP for both methods, and IOP fluctuation was recorded as highest IOP minus lowest IOP. With the comparison of diagnostic tests, both normal eyes and glaucoma suspect groups, the peak IOP was caught at 15min. Comparative analysis of both groups also showed that the peak IOP measurements were statistically higher for the WDT compared to mean diurnal IOP (P=0.039, P=0.048 under normal group and P=0.032 and P=0.031 under glaucoma suspect group). Similarly, the WDT had a statistically higher mean IOP fluctuation score than the mean diurnal IOP method in both groups (P=0.003, P=0.011 under normal group; P=0.002 and P=0.005 under glaucoma suspect group). This study shows that WDT is a comparable diagnostic exam in predicting IOP fluctuations than mean diurnal measurement. WDT is a promising diagnostic procedure for risk assessment in glaucoma.

Glaucoma Assessment from Fundus Images with Fundus to OCT Feature Space Mapping

Early detection and treatment of glaucoma is of interest as it is a chronic eye disease leading to an irreversible loss of vision. Existing automated systems rely largely on fundus images for assessment of glaucoma due to their fast acquisition and cost-effectiveness. Optical Coherence Tomographic ( OCT ) images provide vital and unambiguous information about nerve fiber loss and optic cup morphology, which are essential for disease assessment. However, the high cost of OCT is a deterrent for deployment in screening at large scale. In this article, we present a novel CAD solution wherein both OCT and fundus modality images are leveraged to learn a model that can perform a mapping of fundus to OCT feature space. We show how this model can be subsequently used to detect glaucoma given an image from only one modality (fundus). The proposed model has been validated extensively on four public andtwo private datasets. It attained an AUC/Sensitivity value of 0.9429/0.9044 on a diverse set of 568 images, which is superior to the figures obtained by a model that is trained only on fundus features. Cross-validation was also done on nearly 1,600 images drawn from a private (OD-centric) and a public (macula-centric) dataset and the proposed model was found to outperform the state-of-the-art method by 8% (public) to 18% (private). Thus, we conclude that fundus to OCT feature space mapping is an attractive option for glaucoma detection.

Retinal Nerve Fibre Layer Assessment in Glaucoma and Glaucoma Suspect by Optical Coherence Tomography (OCT)

Abstract Background Glaucoma is a progressive optic neuropathy characterized by structural changes in the optic nerve head, retinal nerve fiber layer (RNFL) defect, and accompanying visual field damage (VFD). In glaucoma management, detecting progression is essential in both early and late stages of the disease. In patients with an established diagnosis of glaucoma, evidence of progression will influence a clinician’s decision whether to modify glaucoma therapy. In patients who are suspected of having the disease, progression detection can confirm the diagnosis, and help decide how to manage the patient. Although standard automated perimetry has been the most commonly used test to monitor glaucomatous progression, evidence suggests that in some eyes substantial structural damage can be detected before the development of clinically detectable VFD. Objective To compare retinal nerve fibre layer thickness between glaucoma and glaucoma suspects. Methods We conducted our cross sectional comparative study on Forty eight eyes from patients recruited from Ain Shams University Hospitals, Ophthalmology Department during the period from February 2019 to December 2019. The Forty eight eyes were recruited in this study and divided into 2 groups. Group A- (Twenty-four eyes with moderate open angle glaucoma) and Group B- (Twenty four eyes with criteria of glaucoma suspects). Results There was highly Significant difference as regards OCT parameters of the patients (p &amp;lt; 0.001 for all). As the mean value of Cup/disc ratio was lower in Glaucoma Suspected cases, while Total RNFL, RNFL Superior and RNFL Inferior was higher in Glaucoma Suspected cases than patients with Moderate glaucoma. This study showed that; by using ROC-curve analysis, cup to disc ratio (CDR) at a cutoff point (&amp;gt;0.59) detected patients with glaucoma, with excellent accuracy, sensitivity= 95% and specificity= 100%. Also, by using ROC-curve analysis, superior RNFL at a cutoff point (≤116) detected patients with glaucoma, with fair accuracy, sensitivity= 66% and specificity= 91%. While inferior RNFL at a cutoff point (≤113) detected patients with glaucoma, with good accuracy, sensitivity= 70% and specificity= 95%. Conclusion In conclusion, structural progression measured using SD OCT was associated with functional progression shown in VF loss in glaucoma suspect or preperimetric glaucoma eyes and in glaucomatous eyes. Average, inferior and superior RNFL attenuations were the most important risk factors for visual field progression.