Retinal Conditions Research Articles

Cataract surgery in patients with diabetes mellitus. Prevention and optimisation of the diabetic macular edema therapy

Diabetes mellitus (DM) is a major public health problem, with approximately one third having signs of diabetic retinopathy (DR). In such patients, cataracts develop at an earlier age and 2–5 times more frequently. The incidence of macular edema (ME) after phacoemulsification of cataracts (FEC) in patients with diabetes has been shown to increase by 1.80 times and, in the presence of DR by 6.23. The critical period for retinal deterioration is 2 months after FEC, which requires an active therapy and monitoring. For patients with pre-existing diabetic macular edema (DME), If the cataract does not affect daily activities of patients with a pre-existing DME and the optical clarity is adequate, it is preferable to postpone surgical treatment in order to maximize retinal stabilization on OCT at two consecutive visits with a month’s interval between. In the case of severe impairment of lens transparency, FEC with an intravitreal injection of anti-VEGF 28 days or steroids 1 month before surgery under strict monitoring of the retinal condition using OCT is recommended. If macular changes are absent and there is a risk of developing DME, the use of nonsteroid anti-inflammatory drugs is necessary. Prophylactic intravitreal therapy is unacceptable in the absence of MO.

Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach.

Artificial intelligence has been used effectively in medical diagnosis. The objective of this project is to examine the application of a collective AI model using weighted fusion of predicted probabilities from different AI architectures to diagnose various retinal conditions based on optical coherence tomography (OCT). A publicly available Noor dataset, comprising 16,822, images from 554 retinal OCT scans of 441 patients, was used to predict a diverse spectrum of age-related macular degeneration (AMD) stages: normal, drusen, or choroidal neovascularization. These predictions were compared with predictions from ResNet, EfficientNet, and Attention models, respectively, using precision, recall, F1 score, and confusion matric and receiver operating characteristics curves. Our collective model demonstrated superior accuracy in classifying AMD compared to individual ResNet, EfficientNet, and Attention models, showcasing the effectiveness of using trainable weights in the ensemble fusion process, where these weights dynamically adapt during training rather than being fixed values. Specifically, our ensemble model achieved an accuracy of 91.88%, precision of 92.54%, recall of 92.01%, and F1 score of 92.03%, outperforming individual models. Our model also highlights the refinement process undertaken through a thorough examination of initially misclassified cases, leading to significant improvements in the model's accuracy rate to 97%. This study also underscores the potential of AI as a valuable tool in ophthalmology. The proposed ensemble model, combining different mechanisms highlights the benefits of model fusion for complex medical image analysis.

American Society of Retina Specialists Clinical Practice Guidelines on Multimodal Imaging for Retinal Disease.

Purpose: Advancements in retinal imaging have augmented our understanding of the pathology and structure-function relationships of retinal disease. No single diagnostic test is sufficient; rather, diagnostic and management strategies increasingly involve the synthesis of multiple imaging modalities. Methods: This literature review and editorial offer practical clinical guidelines for how the retina specialist can use multimodal imaging to manage retinal conditions. Results: Various imaging modalities offer information on different aspects of retinal structure and function. For example, optical coherence tomography (OCT) and B-scan ultrasonography can provide insights into the microstructural anatomy; fluorescein angiography (FA), indocyanine green angiography (ICGA), and OCT angiography (OCTA) can reveal vascular integrity and perfusion status; and near-infrared reflectance and fundus autofluorescence (FAF) can characterize molecular components within tissues. Managing retinal vascular diseases often includes fundus photography, OCT, OCTA, and FA to evaluate for macular edema, retinal ischemia, and the secondary complications of neovascularization (NV). OCT and FAF play a key role in diagnosing and treating maculopathies. FA, OCTA, and ICGA can help identify macular NV, posterior uveitis, and choroidal venous insufficiency, which guides treatment strategies. Finally, OCT and B-scan ultrasonography can help with preoperative planning and prognostication in vitreoretinal surgical conditions. Conclusions: Today, the retina specialist has access to numerous retinal imaging modalities that can augment the clinical examination to help diagnose and manage retinal conditions. Understanding the capabilities and limitations of each modality is critical to maximizing its clinical utility.

Minimum intensity projection of embossed quadrant-detection images for improved photoreceptor mosaic visualisation.

Non-confocal split-detection imaging reveals the cone photoreceptor inner segment mosaic in a plethora of retinal conditions, with the potential of providing insight to ageing, disease, and response to treatment processes, in vivo, and allows the screening of candidates for cell rescue therapies. This imaging modality complements confocal reflectance adaptive optics scanning light ophthalmoscopy, which relies on the waveguiding properties of cones, as well as their orientation toward the pupil. Split-detection contrast, however, is directional, with each cone inner segment appearing as opposite dark and bright semicircles, presenting a challenge for either manual or automated cell identification. Quadrant-detection imaging, an evolution of split detection, could be used to generate images without directional dependence. Here, we demonstrate how the embossed-filtered quadrant-detection images, originally proposed by Migacz etal. for visualising hyalocytes, can also be used to generate photoreceptor mosaic images with better and non-directional contrast for improved visualisation. As a surrogate of visualisation improvement between legacy split-detection images and the images resulting from the method described herein, we provide preliminary results of simple image processing routines that may enable the automated identification of generic image features, as opposed to complex algorithms developed specifically for photoreceptor identification, in pathological retinas.

Frosted branch angiitis in a patient with systemic juvenile idiopathic arthritis: a case report

BackgroundFrosted branch angiitis is a retinal vascular condition that is associated with a viral infection or autoimmune disorders like Crohn’s disease, systemic lupus erythematosus, and Behcet’s disease. Frosted branch angiitis presents with vascular inflammation, retinal edema, and severe retinal vascular sheathing. We present a case of systemic juvenile idiopathic arthritis, an autoinflammatory disease, presenting with frosted branch angiitis.Report of CaseA 14-year-old female with systemic juvenile idiopathic arthritis and a history of bilateral anterior uveitis developed acute unilateral vision loss and was found to have frosted branch angiitis complicated by branch retinal vein occlusion. She underwent an extensive serology workup and aqueous viral PCR to rule out other possible autoimmune and viral etiologies for forested branch angiitis. She received systemic and intravitreal antiviral treatment due to positive CMV IgM initially. However, the clinical picture improved following the use of a higher dose of oral steroids and the switch of the immunosuppressive agent to a TNF-a inhibitor.ConclusionTo our knowledge, this would be the first case in the literature demonstrating a systemic juvenile idiopathic arthritis patient presenting with frosted branch angiitis. Infectious causes still must be ruled out, especially CMV, as it is the most common cause of secondary frosted branch angiitis.

Angioid Streaks Remain a Challenge in Diagnosis, Management, and Treatment.

Aim: Angioid streaks (ASs) are a rare retinal condition and compromise visual acuity when complicated with choroidal neovascularization (CNV). They represent crack-like dehiscences at the level of the Bruch's membrane. This objective narrative review aims to provide an overview of pathophysiology, current treatment modalities, and future perspectives on this condition. Materials and Methods: A literature search was performed using "PubMed", "Web of Science", "Scopus", "ScienceDirect", "Google Scholar", "medRxiv", and "bioRxiv." Results: ASs may be idiopathic, but they are also associated with systemic conditions, such as pseudoxanthoma elasticum, hereditary hemoglobinopathies, or Paget's disease. Currently, the main treatment is the use of anti-vascular endothelial growth factors (anti-VEGF) to treat secondary CNV, which is the major complication observed in this condition. If CNV is detected and treated promptly, patients with ASs have a good chance of maintaining functional vision. Other treatment modalities have been tried but have shown limited benefit and, therefore, have not managed to be more widely accepted. Conclusion: In summary, although there is no definitive cure yet, the use of anti-VEGF treatment for secondary CNV has provided the opportunity to maintain functional vision in individuals with AS, provided that CNV is detected and treated early.

Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net

ABSTRACT Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based diagnostic test for diabetic retinopathy through an updated UNet framework, adept at scrutinizing fundus images for signs of retinal haemorrhages. The customized UNet underwent GPU training using the IDRiD database, validated against the publicly available DIARETDB1 and IDRiD datasets. Emphasizing the complexity of segmentation, the study employed preprocessing techniques, augmenting image quality and data integrity. Subsequently, the trained neural network showcased a remarkable performance boost, accurately identifying haemorrhage regions with 80% sensitivity, 99.6% specificity, and 98.6% accuracy. The experimental findings solidify the network’s reliability, showcasing potential to alleviate ophthalmologists’ workload significantly. Notably, achieving an Intersection over Union (IoU) of 76.61% and a Dice coefficient of 86.51% underscores the system’s competence. The study’s outcomes signify substantial enhancements in diagnosing critical diabetic retinal conditions, promising profound improvements in diagnostic accuracy and efficiency, thereby marking a significant advancement in automated retinal haemorrhage detection for diabetic retinopathy.

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

Macular Edema is a leading cause of visual impairment and blindness in patients with ocular fundus diseases. Due to its non-invasive and high-resolution characteristics, optical coherence tomography (OCT) has been extensively utilized for the diagnosis of macular diseases. The manual detection of retinal diseases by clinicians is a laborious process, further complicated by the challenging identification of macular diseases. This difficulty arises from the significant pathological alterations occurring within the retinal layers, as well as the accumulation of fluid in the retina. Deep Learning neural networks are utilized for automatic detection of retinal diseases. This paper aims to propose a lightweight hybrid learning Retinal Disease OCT Net with a reduced number of trainable parameters and enable automatic classification of retinal diseases. A Hybrid Learning Retinal Disease OCT Net (RD-OCT) is utilized for the multiclass classification of major retinal diseases, namely neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), retinal vein occlusion (RVO), and normal retinal conditions. The diagnosis of retinal diseases is facilitated by the use of hybrid learning models and pre-trained deep learning models in the field of artificial intelligence. The Hybrid Learning RD-OCT Net provides better accuracy of 97.6% for nAMD, 98.08% for DME, 98% for RVO, and 97% for the Normal group. The respective area under the curve values were 0.99, 0.97, 1.0, and 0.99. The utilization of the RD-OCT model will be useful for ophthalmologists in the diagnosis of prevalent retinal diseases, due to the simplicity of the system and reduced number of trainable parameters.

Inner retinal thickness in Stargardt disease.

To analyze the alterations at the level of the inner retina in patients affected by Stargardt disease (STGD1). Cross-sectional investigation involving STGD1 patients with genetically confirmed diagnosis, who underwent optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), and microperimetry. Overall, 31 patients (62 eyes) with genetically confirmed STGD1 were included in the study. Mean inner retinal thickness, vessel density of plexa, and retinal sensitivity resulted significantly reduced in STGD patients, compared with healthy controls (p < 0.05), both in the outer and in the inner ETDRS rings. Overall, 43% of eyes revealed an inner retinal thinning, whereas 21% and 35% showed a thicker or within normal range inner retina. Inner retina is irregularly altered in STGD1, showing variable quantitative alterations as detected on OCT. Inner retinal status might represent a useful biomarker to better characterize STGD1 and to ascertain the effects of new treatment approaches.

Association of sodium-glucose cotransporter-2 inhibitors and the risk of retinal vascular occlusion: A real-world retrospective cohort study in Taiwan.

Sodium-glucose cotransporter-2 inhibitors (SGLT2is) are proposed to alleviate the development of inflammatory eye diseases. However, the association between SGLT2i and retinal vascular occlusion remains unclear. Therefore, this study aims to explore the effects of SGLT2i on the incidence of retinal vascular occlusion. This retrospective cohort study analysed electronic medical records data from the largest multi-institutional database in Taiwan. Individuals who initiated SGLT2is and dipeptidyl peptidase 4 inhibitors (DPP4is) between 2016 and 2019 were included in our analysis. To conduct a homogenous comparison, inverse probability of treatment weighting with propensity scoring was employed. The primary outcome was retinal vascular occlusion, and the secondary outcomes were retinal vascular occlusion-related complications (macular oedema, vitreous haemorrhage, and tractional retinal detachment) and conditions requiring vitreoretinal intervention (intravitreal injection, retinal laser therapy, and vitrectomy). In total, 12,074 SGLT2i users and 39,318 DPP4i users were included. The incidence rate of retinal vascular occlusion in the SGLT2i and DPP4i groups was 1.2 (95% confidence interval [CI], 0.9-1.4) and 1.6 (95% CI, 1.3-1.8) events per 1000 person-years, respectively, which yielded a subdistribution hazard ratio (SHR) of 0.74 (95% CI, 0.55-0.99). Similar risk reductions were observed in the retinal vascular occlusion-related complications (SHR, 0.76; 95% CI, 0.69-0.84) and conditions requiring vitreoretinal intervention (SHR, 0.84; 95% CI, 0.77-0.94). In this multi-institutional study in Taiwan, SGLT2i use was associated with a reduced risk of retinal vascular occlusion. Further prospective studies are required to ascertain this association.

The Effect of a Loading Dose Regimen in the Switch to Brolucizumab for Patients with Aflibercept-Resistant nAMD.

To evaluate the one-year outcomes of switching to brolucizumab with and without a loading dose regimen (three monthly injections) in eyes with aflibercept-resistant neovascular age-related macular degeneration (nAMD). We retrospectively studied nAMD patients who had retinal exudate under bimonthly injections of aflibercept and were switched to brolucizumab from aflibercept. Patients were grouped into intravitreal brolucizumab injection (IVBr) with a loading dose regimen (loading group) and without a loading dose regimen (nonloading group). We assessed the best-corrected visual acuity (BCVA), central retinal thickness (CRT) at the fovea, subfoveal choroidal thickness (SFCT), IVBr status (number of injections and last injection interval), and retinal exudate status on optical coherence tomography. Overall, 52 eyes received ≥1 IVBr; 26 eyes received ≥3 IVBr with 12-month follow-up. A total of 13 eyes in the loading group and 13 eyes in the nonloading group were reviewed. One year after switching, BCVA changed from 0.28 ± 0.25 to 0.19 ± 0.28 in the loading group (P=0.28) and from 0.25 ± 0.20 to 0.23 ± 0.25 in the nonloading group (P=0.92). The mean CRT decreased from 263.6 ± 40.7 µm to 221.7 ± 54.6 µm in the loading group (P=0.03), while it only changed from 244.9 ± 77.2 µm to 221.0 ± 78.7 µm in the nonloading group (P=0.26). Both the loading and nonloading groups achieved 69% dry macula. The number of injections received was significantly higher in the loading group (7.6 ± 0.6 vs. 6.8 ± 0.4, P < 0.001). Two patients (4.2%) developed intraocular inflammation. Switching to brolucizumab from aflibercept for eyes with nAMD with resistance to bimonthly injections of aflibercept is a valuable treatment option with and without the loading regimen. This trial is registered with UMIN000023676.

Glucagon Increases Retinal Rod Bipolar Cell Inhibition Through a D1 Dopamine Receptor-Dependent Pathway That Is Altered After Lens-Defocus Treatment in Mice.

Members of the secretin/glucagon family have diverse roles in retinal physiological and pathological conditions. Out of them, glucagon has been associated with eye growth regulation and image defocus signaling in the eye, both processes central in myopia induction. On the other hand, dopamine is perhaps the most studied molecule in myopia and has been proposed as fundamental in myopia pathogenesis. However, glucagonergic activity in the mammalian retina and its possible link with dopaminergic signaling remain unknown. To corroborate whether glucagon and dopamine participate together in the modulation of synaptic activity in the retina, inhibitory post-synaptic currents were measured in rod bipolar cells from retinal slices of wild type and negative lens-exposed mice, using whole cell patch-clamp recordings and selective pharmacology. Glucagon produced an increase of inhibitory post-synaptic current frequency in rod bipolar cells, which was also dependent on dopaminergic activity, as it was abolished by dopamine type 1 receptor antagonism and under scotopic conditions. The effect was also abolished after 3-week negative lens-exposure but could be recovered using dopamine type 1 receptor agonism. Altogether, these results support a possible neuromodulatory role of glucagon in the retina of mammals as part of a dopaminergic activity-dependent synaptic pathway that is affected under myopia-inducing conditions.

Current use of aflibercept 8 mg (eylea HD) in retinal diseases

Aflibercept 8 mg (Eylea HD) has emerged as a pivotal agent in the treatment of retinal diseases and has begun to showcase its efficacy and innovation in the field of ophthalmology. This article delves into the current use of aflibercept 8 mg, exploring its applications, mechanisms of action, and impact on the treatment landscape for various retinal conditions. This review provides an overview of recent clinical studies and highlights the key findings and advancements associated with the use of 8 mg aflibercept.

Combination of blockade of endothelin signalling and compensation of IGF1 expression protects the retina from degeneration.

Retinitis pigmentosa (RP) and macular dystrophy (MD) cause severe retinal dysfunction, affecting 1 in 4000 people worldwide. This disease is currently assumed to be intractable, because effective therapeutic methods have not been established, regardless of genetic or sporadic traits. Here, we examined a RP mouse model in which the Prominin-1 (Prom1) gene was deficient and investigated the molecular events occurring at the outset of retinal dysfunction. We extracted the Prom1-deficient retina subjected to light exposure for a short time, conducted single-cell expression profiling, and compared the gene expression with and without stimuli. We identified the cells and genes whose expression levels change directly in response to light stimuli. Among the genes altered by light stimulation, Igf1 was decreased in rod photoreceptor cells and astrocytes under the light-stimulated condition. Consistently, the insulin-like growth factor (IGF) signal was weakened in light-stimulated photoreceptor cells. The recovery of Igf1 expression with the adeno-associated virus (AAV) prevented photoreceptor cell death, and its treatment in combination with the endothelin receptor antagonist led to the blockade of abnormal glial activation and the promotion of glycolysis, thereby resulting in the improvement of retinal functions, as assayed by electroretinography. We additionally demonstrated that the attenuation of mammalian/mechanistic target of rapamycin (mTOR), which mediates IGF signalling, leads to complications in maintaining retinal homeostasis. Together, we propose that combinatorial manipulation of distinct mechanisms is useful for the maintenance of the retinal condition.

Diabetic eye disease in computerised tomography of feature extraction and classification in hybrid neural network

ABSTRACT Blindness or loss of vision is primarily caused by diabetic retinopathy, or DR. It is a retinal condition that may cause harm to the eyes’ blood vessels.Due to the complicated eye structure, manually detecting diabetic retinopathy takes time and is prone to human mistake. There have been several automatic methods proposed for the diagnosis of diabetic retinopathy from fundus images.Using a combination of InceptionV3 and ResNet50, an optimised deep Convolutional Neural Network model of feature fusion is presented in this paper to categories diabetic retinopathy photos into five kinds. Concatenating results in the suggested model including both the Inception Modules and the Residual Blocks, this mitigates the gradient problem and reduces the number of parameters. Resnet50 and Inceptionv3, two different deep learning (DL) models, were used. The proposed hybrid InceptionV3 and ResNet50 based optimised deep CNN (IR_ODCNN) model gathers features from the data and joins them before sending them to the DCNN that has been optimised for classification. The hyper parameters for CNN layer training are optimised using the Grey Wolf Optimizer (GWO) method. An image dataset of the fundus is used to assess the suggested model. The experimental findings indicate that, when compared to the current approaches, In terms of classifying diabetic retinopathy, the proposed model performs better.

Recent advances in the use of optical coherence tomography in neuro-ophthalmology: A review.

Optical coherence tomography (OCT) is an in vivo imaging modality that provides non-invasive, high resolution and fast cross-sectional images of the optic nerve head, retina and choroid. OCT angiography (OCTA) is an emerging tool. Itis a non-invasive, dye-free imaging approach of visualising the microvasculature of the retina and choroid by employing motion contrast imaging for blood flow detection and is gradually receiving attention for its potential roles in various neuro-ophthalmic and retinal conditions. We will review the clinical utility of the OCT in the management of various common neuro-ophthalmic and neurological disorders. We also review some of the OCTA research findings in these conditions. Finally, we will discuss the limitations of OCT as well as introduce other emerging technologies.

Clinical features of three-layer’s impairment of capillary plexuses’ perfusion at follow-up in patients with Purcher retinopathy and Purcher-like retinopathy

In Purcher retinopathy and Purcher-like retinopathy, ischemic lesions occur at the level of all three retina capillary plexuses. Recognition of characteristic ischemic patterns by optical coherence tomography helps to predict the degree of visual functions’ recovery. The aim of this study is to present clinical cases of patients with Purcher retinopathy and Purcher-like retinopathy and to observe the dynamics of visual rehabilitation of patients with retinal vascular conditions. Two patients with complaints of central vision loss and skotomata in the visual field were examined. The complex of multimodal visualization included fundus photoregistration with fundus camera, optical coherence tomography and optical coherence tomography-angiography, and microperimetry. Paracentral acute middle maculopathy and acute macular neuroretinopathy are the manifestations of the same ischemic process on different levels of retinal vascular plexuses. The “diffuse” pattern damages in paracentral acute middle maculopathy lead to more permanent structural and functional impairments opposed to the “skip” pattern. The optical coherence tomography allows to predict the possibilities of visual and functional rehabilitation in patients with ischemic damage of retinal vessels.

Multi-label classification of retinal disease via a novel vision transformer model.

The precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the classification of retinal images pertaining to a specific retinal condition. In clinical practice, it is not uncommon for a single patient to present with multiple retinal disorders concurrently. Hence, the task of classifying retinal images into multiple labels remains a significant obstacle for existing methodologies, but its successful accomplishment would yield valuable insights into a diverse array of situations simultaneously. This study presents a novel vision transformer architecture called retinal ViT, which incorporates the self-attention mechanism into the field of medical image analysis. To note that this study supposed to prove that the transformer-based models can achieve competitive performance comparing with the CNN-based models, hence the convolutional modules have been eliminated from the proposed model. The suggested model concludes with a multi-label classifier that utilizes a feed-forward network architecture. This classifier consists of two layers and employs a sigmoid activation function. The experimental findings provide evidence of the improved performance exhibited by the suggested model when compared to state-of-the-art approaches such as ResNet, VGG, DenseNet, and MobileNet, on the publicly available dataset ODIR-2019, and the proposed approach has outperformed the state-of-the-art algorithms in terms of Kappa, F1 score, AUC, and AVG.

Optical coherence tomography imaging of the peripheral retina.

This review critically assesses the applicability of retinal periphery imaging technology, scrutinizing its practical limitations and potential advancements within ophthalmology. It underscores the significant costs and the need to evaluate the clinical utility of optical coherence tomography (OCT) and OCT angiography devices. It emphasizes how clinicians should consider their practice-specific use-cases while investing in devices with capabilities like ultra-widefield OCT, autofluorescence imaging, and angiography. The paper also highlights the challenges associated with image acquisition, such as artifact management and patient cooperation for extended fixation periods. This review outlines the utility of these modalities in various retinal pathologies, as well as their contribution to telemedicine and personalized care, facilitated by artificial intelligence for improved image processing, quantification, and interpretation. These techniques potentially offer a more comprehensive understanding of peripheral retinal conditions and associated pathologies, thus influencing clinical decision-making, particularly in remote regions with limited specialist access.

Glaucoma Detection Based on Texture Feature of Neuro Retinal Rim Area in Retinal Fundus Image

One method for detecting glaucoma is by comparing ratios in the area of neuroretinal rim. Comparing area ratios in the neuroretinal rim is difficult for ophthalmologists since it requires high accuracy and is highly dependent on the patient's retinal condition. In this study, we sought to perform neuro retinal rim feature extraction based on histogram and gray level co-occurrence matrix (GLCM) of normal retinal images and glaucoma, automatically distinguish between normal eyes and eyes with glaucoma, and evaluate the method's validity using the measures of accuracy, sensitivity, and specificity We adopted a machine learning approach in conducting automatic feature extraction of the retinal rim through three main stages: 1) image acquisition, 2) pre-processing, and 3) classification. We used a dataset from RIM-ONE for normal eyes images and DRISTHI-GS for glaucoma images.Classification was carried out on 154 images (80 images for glaucoma images and 74 images for normal images). Regarding true positive, false negative, false positive, and true negative, we examined the sensitivity, specificity, and accuracy of automatic extraction and classification. The highest findings are 96.10%, 98.75%, and 93.24%, respectively. This study showed that automatic texture features and classification are possible, accurate and important in detecting glaucoma.