Optic Disc Cupping Research Articles

Chákṣu: A glaucoma specific fundus image database

We introduce Chákṣu–a retinal fundus image database for the evaluation of computer-assisted glaucoma prescreening techniques. The database contains 1345 color fundus images acquired using three brands of commercially available fundus cameras. Each image is provided with the outlines for the optic disc (OD) and optic cup (OC) using smooth closed contours and a decision of normal versus glaucomatous by five expert ophthalmologists. In addition, segmentation ground-truths of the OD and OC are provided by fusing the expert annotations using the mean, median, majority, and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The performance indices show that the ground-truth agreement with the experts is the best with STAPLE algorithm, followed by majority, median, and mean. The vertical, horizontal, and area cup-to-disc ratios are provided based on the expert annotations. Image-wise glaucoma decisions are also provided based on majority voting among the experts. Chákṣu is the largest Indian-ethnicity-specific fundus image database with expert annotations and would aid in the development of artificial intelligence based glaucoma diagnostics.

Dual Consistency Enabled Weakly and Semi-Supervised Optic Disc and Cup Segmentation with Dual Adaptive Graph Convolutional Networks.

Glaucoma is a progressive eye disease that results in permanent vision loss, and the vertical cup to disc ratio (vCDR) in colour fundus images is essential in glaucoma screening and assessment. Previous fully supervised convolution neural networks segment the optic disc (OD) and optic cup (OC) from color fundus images and then calculate the vCDR offline. However, they rely on a large set of labeled masks for training, which is expensive and time-consuming to acquire. To address this, we propose a weakly and semi-supervised graph-based network that investigates geometric associations and domain knowledge between segmentation probability maps (PM), modified signed distance function representations (mSDF), and boundary region of interest characteristics (B-ROI) in three aspects. Firstly, we propose a novel Dual Adaptive Graph Convolutional Network (DAGCN) to reason the long-range features of the PM and the mSDF w.r.t. the regional uniformity. Secondly, we propose a dual consistency regularization-based semi-supervised learning paradigm. The regional consistency between the PM and the mSDF, and the marginal consistency between the derived B-ROI from each of them boost the proposed model's performance due to the inherent geometric associations. Thirdly, we exploit the task-specific domain knowledge via the oval shapes of OD & OC, where a differentiable vCDR estimating layer is proposed. Furthermore, without additional annotations, the supervision on vCDR serves as weakly-supervisions for segmentation tasks. Experiments on six large-scale datasets demonstrate our model's superior performance on OD & OC segmentation and vCDR estimation. The implementation code has been made available 1.

Relation Between Sleep Position and Intraocular Pressure in People with Glaucoma

Glaucoma still be the second most common cause of blindness in the world. Glaucoma is a group of neuropathic diseases of the optic nerve which is characterized by the extraction of the optic disc (cupping) may causes visual disturbances. Intraocular pressure (IOP) is the most influential factor of glaucoma and affected by various factors, especially body position during sleep. Sleep composed of REM sleep and NREM sleep is related to muscle tone and psychological activity that has impact for sleep posture. The increase of intraocular pressure is directly proportional to the increase of episcleral venous pressure. The increase of IOP in prone position is probably due to an increase in choroidal vascular volume and increase of episcleral venous pressure, whereas in the lateral decubitus position the increase in IOP is caused by compression of the jugular veins when using a pillow. IOP rises when move to the lateral decubitus position in the dependent eye was 1 mmHg and in the non-dependent eye was 2 mmHg. The angle of the head to the bed has an effect on increasing IOP, in the vertical head-down position there can be an increase of 70% due to the force of gravity, whereas in patients who sleep with their head at a higher angle to the mattress, the IOP will decrease compared to the horizontal sleeping position.

Acquired Lens Zonular Loss with Bean Pot Optic Disc Cupping in Congenital Glaucoma

Acquired Lens Zonular Loss with Bean Pot Optic Disc Cupping in Congenital Glaucoma

Awareness and knowledge of glaucoma among visitors of main public hospitals in Damascus, Syria: a cross-sectional study

BackgroundThe main way to prevent blindness from Glaucoma is by early detection and diagnosis; and to do so the awareness must be raised among people where Glaucoma is defined as an acquired chronic optic neuropathy characterized by optic disk cupping and visual field loss. Lack of knowledge about this disease is one of the most important reasons that made it develop to advanced stages. Based on that, we conducted scientific research to assess peoples’ awareness and knowledge about Glaucoma.After reviewing the literature, it was found that this study is the first in Syria.MethodsWe conducted a cross-sectional study in May 2022. The study included participants, who are above 20 years old, from visitors of Al-Mouwasat University Hospital in Damascus, Syria. During one week, Data was gathered according to the questionnaire, which was presented through face-to-face interviews with participants. We have allocated one point (1) to each question. Three levels of knowledge were adopted, namely; weak level from (0) to (3), average level from (4) to (7) and good level from (8) to (11). Associations between participants' demographic and other details were tested using Chi-square test and other tests, and a p-value of < 0.05 was considered significant.ResultsA total of 500 participants were interviewed. For awareness of Glaucoma, 33.6% of the participants (n = 168) had heard of Glaucoma, and 66.4% hadn’t (n = 332). Mean test results for the knowledge of Glaucoma was 1.62 out of 11, and only 8% of participants (n = 40) had a good knowledge of Glaucoma. Education Level, governorate, and department of hospital that the patient came for significantly affected the knowledge of Glaucoma. Moreover, hospital, Ophthalmologists' Clinics, and health staff (M = 5.45) were the better Resource for information than family, relatives, and friends (M = 3.16). Finally, social media and the Internet group had the lowest mean test results (M = 1.23). These test results were significant, with a p-value < 0.001.ConclusionThe percentage of knowledge and awareness was significantly low. Organized community awareness methods must disseminate more ability to increase the general public's understanding to avoid injury and late diagnosis of Glaucoma.

Screening fundus photography predicts and reveals risk factors for glaucoma conversion in eyes with large optic disc cupping

This study aimed to investigate the risk factors for glaucoma conversion and progression in eyes with large optic disc cupping without retinal nerve fiber layer defect (RNFLD). Five hundred forty-two eyes of 271 subjects who had a vertical cup-to-disc ratio (CDR) ≥ 0.6 without RNFLD were enrolled. Characteristics for optic disc configuration (including CDR, vertical cupping, ISNT rule, disc ovality, peripapillary atrophy [PPA]-to-disc area [DA] ratio, and lamina cribrosa pore visibility) and blood vessels (including central retinal vessel trunk [CRVT] nasalization, bayoneting of vessels, baring of circumlinear vessels, history of disc hemorrhage [DH] and vessel narrowing/sclerotic change) were evaluated. From a median follow-up of 11.3 years, 26.6% of eyes (n = 144) developed RNFLD within a median of 5.1 years. Baseline factors, including vertical CDR ≥ 0.7 (hazard ratio [HR] = 2.12), vertical cupping (HR = 1.93), ISNT rule violation (HR = 2.84), disc ovality ≥ 1.2 (HR = 1.61), PPA-to-DA ratio ≥ 0.4 (HR = 1.77), CRVT nasalization ≥ 60% (HR = 1.77), vessel narrowing/sclerotic change (HR = 2.13), DH history (HR = 5.60), and baseline intraocular pressure ≥ 14 mmHg (HR = 1.70) were significantly associated with glaucoma conversion (all Ps < 0.05). An HR-matched scoring system based on initial fundus photography predicted glaucoma conversion with specificity of 90.4%. Careful examination of the optic nerve head and vascular structures can help to predict the risk of glaucoma conversion in eyes with large optic disc cupping.

Evaluation of Peripapillary and Macular Nerve Fiber Layer Thickness in Paediatric Glaucoma Suspects

Introduction: Glaucoma patients are known to have optic disc cupping, leading to loss of retinal ganglion cell axons. This peripapillary nerve thinness is known to be associated with glaucoma in adult patients. Hence, we intended to evaluate the peripapillary and macular nerve fiber thickness in eyes of pediatric glaucoma suspects using spectral domain optical coherence tomography (OCT). Methods: This cross-sectional analytical study included 44 eyes of 22 paediatric glaucoma suspects, who were compared with 40 eyes of 20 normal paediatric eyes. Pearson correlation coefficients were calculated to assess the relations between the peripapillary retinal nerve fiber layer (RNFL) thickness and ganglion cell complex (GCC). Receiver Operating Characteristics (ROC) curve was plotted for OCT parameters in both the groups along with area under the curve (AUC) calculation. P values &lt; 0.05 were considered as statistically significant. Results: We found the RNFL to be thickest in the superior, inferior, nasal and temporal quadrants in both glaucoma suspects and normal group. When compared among quadrants in RNFL, all the values in different quadrants were statistically significant from each other and in between groups with p &lt; 0.01. The RNFL thickness was statistically different in glaucoma when compared with normal in superior, temporal, and nasal quadrants however, no statistically significant difference was found in inferior quadrant RNFL. The largest AUC for discrimination of glaucoma suspect eyes from normal in peripapillary RNFL in was nasal quadrant in right eye, followed by temporal quadrant whereas it was largest in superior followed by nasal quadrants then average in left eye. However, AUC for discrimination of glaucoma suspects from normal in GCC was poor. Conclusions: Glaucoma suspect paediatric eyes showed significant thinning in peripapillary nerve fiber layer thickness compared to normal subjects.

Polarformer: Optic Disc and Cup Segmentation Using a Hybrid CNN-Transformer and Polar Transformation

The segmentation of optic disc (OD) and optic cup (OC) are used in the automatic diagnosis of glaucoma. However, the spatially ambiguous boundary and semantically uncertain region-of-interest area in pictures may lead to the degradation of the performance of precise segmentation of the OC and OD. Unlike most existing methods, including the variants of CNNs (Convolutional Neural Networks) and U-Net, which limit the contributions of rich global features, we instead propose a hybrid CNN-transformer and polar transformation network, dubbed as Polarformer, which aims to extract discriminative and semantic features for robust OD and OC segmentation. Our Polarformer typically exploits contextualized features among all input units and models the correlation of structural relationships under the paradigm of the transformer backbone. More specifically, our learnable polar transformer module optimizes the polar transformations by sampling images in the Cartesian space and then mapping them back to the polar coordinate system for masked-image reconstruction. Extensive experimental results present that our Polarformer achieves superior performance in comparison to most state-of-the-art methods on three publicly available datasets.

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

Recently, the development of a rapid detection approach for glaucoma has been widely proposed to assist medical personnel in detecting glaucoma disease thanks to the outstanding performance of artificial intelligence. In several glaucoma detectors, cup-to-disc ratio (CDR) and disc damage likelihood scale (DDLS) play roles as the major objects that are used to analyze glaucoma. However, using CDR and DDLS is quite difficult since every person has different characteristics (shape, size, etc.) of the optic disc and optic cup. To overcome this issue, we proposed an alternative way to detect glaucoma disease by analyzing the damage to the retinal nerve fiber layer (RNFL). Our proposed method is divided into two processes: (1) the pre-treatment process and (2) the glaucoma classification process. We started the pre-treatment process by removing unnecessary parts, such as the optic disc and blood vessels. Both parts are considered for removal since they might be obstacles during the analysis process. For the classification stages, we used nine deep-learning architectures. We evaluated our proposed method in the ORIGA dataset and achieved the highest accuracy of 92.88% with an AUC of 89.34%. This result is improved by more than 15% from the previous research work. Finally, it is expected that our model could help improve eye disease diagnosis and assessment.

Exploring deep feature-blending capabilities to assist glaucoma screening

Over the last three decades, computer vision has had a vital role in the healthcare sector by providing soft computing-based robust and intelligent diagnostic solutions. Glaucoma is a critical ophthalmic disease that can trigger irreversible loss of vision. The number of patients with glaucoma is increasing dramatically worldwide. Manual ophthalmic assessment of glaucoma detection is a tedious, error-prone, time-consuming, and subjective task. Therefore, computer-assisted automatic glaucoma diagnosis methods are required to strengthen existing diagnostic methods with their robust performance. Optic disc (OD) and optic cup (OC) segmentation have a key role in glaucoma detection. Accurate segmentation of the OD and OC provides valuable computational and clinical details that can substantially assist in the glaucoma screening process. Retinal fundus images have extensive variations in terms of size, shape, pixel intensity values, and background effects that make segmentation challenging. To mitigate these challenges, we developed two novel networks for accurate OD and OC segmentation. An efficient shallow segmentation network (ESS-Net) is the base network whereas a feature-blending-based shallow segmentation network (FBSS-Net) is the final network of this work. ESS-Net is a shallow architecture with a maximum-depth semantic preservation block for accurate segmentation, while FBSS-Net uses internal and external feature blending to improve overall segmentation performance.To confirm their effectiveness, we evaluated both networks using four publicly available datasets; REFUGE, Drions-DB, Drishti-GS, and Rim-One-r3. The proposed methods exhibited excellent segmentation performance, requiring a small number of trainable parameters (3.02 million parameters).

RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm

BackgroundGlaucoma can cause irreversible blindness to people’s eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough.MethodsThis paper proposes a new encoder–decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder–decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup.ResultsThe experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively.ConclusionsThis paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective.

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs.

Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder-Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.

An End-to-End Real-Time Lightweight Network for the Joint Segmentation of Optic Disc and Optic Cup on Fundus Images

Glaucoma is the second-most-blinding eye disease in the world and accurate segmentation of the optic disc (OD) and optic cup (OC) is essential for the diagnosis of glaucoma. To solve the problems of poor real-time performance, high algorithm complexity, and large memory consumption of fundus segmentation algorithms, a lightweight segmentation algorithm, GlauNet, based on convolutional neural networks, is proposed. The algorithm designs an efficient feature-extraction network and proposes a multiscale boundary fusion (MBF) module, which greatly improves the segmentation efficiency of the algorithm while ensuring segmentation accuracy. Experiments show that the algorithm achieves Dice scores of 0.9701/0.8959, 0.9650/0.8621, and 0.9594/0.8795 on three publicly available datasets—Drishti-GS, RIM-ONE-r3, and REFUGE-train—for both the optic disc and the optic cup. The number of model parameters is only 0.8 M, and it only takes 13 ms to infer an 800 × 800 fundus image on a GTX 3070 GPU.

An Improved Disc Segmentation Based on U-Net Architecture for Glaucoma Diagnosis

Various computer-aided diagnosis systems have been expanded and used for diagnosing glaucoma. Since the optic disc and optic cup are the main parameters for the early detection of glaucoma, this study proposes an accurate CAD system that firstly detects the optic disc and cup then classifies them into normal or abnormal. The U-Net architecture is employed. Despite its excellent segmentation performances, this model repeatedly extracts low-level features, which leads to redundant use of computational sources. To address these issues, a two-stage segmentation of the optic disc and cup was proposed. Firstly, a region of interest (ROI) is extracted from the fundus images by localizing and cutting the optic disc zone. Then, a U-Net model was built in order to obtain the refined segmentation. The public REFUGE dataset is adopted to validate proposed system. After a data augmentation step, an average accuracy of 0.97 and 0.96 for predicted OD cut off area and predicted original images respectively are obtained.

Progression of functional and structural glaucomatous damage in relation to diurnal and nocturnal dips in mean arterial pressure.

Systemic hypoperfusion plays a pivotal role in the pathogenesis of primary open-angle glaucoma (POAG). Extreme dips in mean arterial pressure (MAP) due to high 24-h variability are associated with POAG, however, whether this is driven by diurnal or nocturnal dips remains undocumented. We aimed this study to investigate the association of POAG damage with variability and dips in the diurnal and nocturnal MAP. We conducted a retrospective longitudinal study that included 110 POAG patients who underwent 24-h ambulatory blood pressure monitoring. Our outcomes included (i) functional [visual field defects expressed as mean deviation (MD)] and (ii) structural (optic disc cupping obtained from cup-to-disc ratio) glaucoma damage. MAP variability independent of the mean (VIMmap) was computed for diurnal and nocturnal MAP. Dips were the five diurnal and three nocturnal lowest drops in MAP. We also calculated the night-to-day ratio. We applied mixed models to evaluate the progression of visual field defects and optic disc cupping in relation to diurnal and nocturnal MAP measures. The mean age was 64.0 y (53% women). The median follow-up was 9 years. In adjusted mixed models, functional progression of glaucoma damage was associated with VIMmap (-2.57 dB change in MD per every 3 mmHg increase in VIMmap; P < 0.001) and diurnal MAP dips (changes in the MD ranged from -2.56 to -3.19 dB; P < 0.001). Every 5 mmHg decrease in the nocturnal MAP level was associated with -1.14 dB changes in MD [95% confidence interval (CI), -1.90 to -0.40] and 0.01 larger optic disc cupping (95% CI, 0.01-0.02). Lower night-to-day ratio was also related to both outcomes (P ≤ 0.012). Functional glaucoma damage worsened if nocturnal hypotension was combined with high variability or extreme dips in the diurnal MAP (P ≤ 0.022). Progression of glaucoma damage in POAG associates with high variability and extreme dips in the diurnal MAP. Structural glaucoma damage seems more vulnerable to nocturnal hypotension. Ambulatory blood pressure monitoring allows the assessment of sporadic diurnal and persistent nocturnal hypotension episodes. These phenotypes might offer an opportunity to improve the risk-stratification of open-angle glaucoma (OAG).

Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification

Glaucoma is a prevalent cause of blindness worldwide. If not treated promptly, it can cause vision and quality of life to deteriorate. According to statistics, glaucoma affects approximately 65 million individuals globally. Fundus image segmentation depends on the optic disc (OD) and optic cup (OC). This paper proposes a computational model to segment and classify retinal fundus images for glaucoma detection. Different data augmentation techniques were applied to prevent overfitting while employing several data pre-processing approaches to improve the image quality and achieve high accuracy. The segmentation models are based on an attention U-Net with three separate convolutional neural networks (CNNs) backbones: Inception-v3, visual geometry group 19 (VGG19), and residual neural network 50 (ResNet50). The classification models also employ a modified version of the above three CNN architectures. Using the RIM-ONE dataset, the attention U-Net with the ResNet50 model as the encoder backbone, achieved the best accuracy of 99.58% in segmenting OD. The Inception-v3 model had the highest accuracy of 98.79% for glaucoma classification among the evaluated segmentation, followed by the modified classification architectures.

Joint optic disc and cup segmentation using feature fusion and attention

Currently, glaucoma is one of the leading causes of irreversible vision loss. So far, glaucoma is incurable, but early treatment can stop the progression of the condition and slow down the speed and extent of vision loss. Early detection and treatment are crucial to prevent glaucoma from developing into blindness. It is an effective method for glaucoma diagnosis to measure Cup to Disc Ratio (CDR) by the segmentation of Optic Disc (OD) and Optic Cup (OC). Compared with OD segmentation, OC segmentation still faces difficulties in segmentation accuracy. In this paper, a deep learning architecture named FAU-Net (feature fusion and attention U-Net) is proposed for the joint segmentation of OD and OC. It is an improved architecture based on U-Net. By adding a feature fusion module in U-Net, information loss in feature extraction can be reduced. The channel and spatial attention mechanisms are combined to highlight the important features related to the segmentation task and suppress the expression of irrelevant regional features. Finally, a multi-label loss is used to generate the final joint segmentation of OD and OC. Experimental results show that the proposed FAU-Net outperforms the state-of-the-art segmentation of OD and OC on Drishti-GS1, REFUGE, RIM-ONE and ODIR datasets.

RMSDSC‐Net: A robust multiscale feature extraction with depthwise separable convolution network for optic disc and cup segmentation

Glaucoma is an eye disease that leads to irreversible vision loss. Accurate Optic Disc (OD) and Optic Cup (OC) segmentation can effectively facilitate ophthalmologist in glaucoma diagnosis. Recently, a series of deep learning approaches attain promising performance in OD and OC segmentation but still face the challenge to precisely segment OC boundary with enhanced computational efficiency. To address this issue, we propose a novel network named Robust Multiscale Feature Extraction with Depthwise Separable Convolution (RMSDSC-Net), which can better solve the challenging tradeoff between segmentation performance and network cost. The proposed RMSDSC-Net is mainly composed of Multiscale Input (MSI), Depthwise Separable Convolution Unit (DSCU), Dilated Convolution Block (DCB), and External Residual Connection (ERC). First, the introduction of MSI can reduce the information loss due to the pooling layers used in the network for capturing rich feature representations. Next, to enhance segmentation performance and computational efficiency, this paper designs DSCU and DCB modules to avoid spatial information loss from minor details of the image and preserve more high-level semantic features. Finally, this paper develops ERC established between the encoding layers and decoding layers to minimize the feature degradation problem. Hence, a high segmentation performance can be achieved using a shallow network. To evaluate the performance of the proposed network, extensive experiments have been enforced on two publicly available databases, DRISHTI-GS and REFUGE. Our approach outperforms the state-of-the-art approaches with the Dice Coefficient of (0.978, 0.919) and (0.965, 0.910) for OD and OC segmentation on DRISHTI-GS and REFUGE databases, respectively. As a result, the proposed approach has a strong potential in analyzing fundus images for glaucoma diagnosis.

The Feasibility of Using Ultra-Widefield Retinal Imaging to Identify Ocular Pathologies amongst Those with Systemic Medical Disease Attending a Tertiary Healthcare Facility at a University Hospital

Aims: The aim of the study was to evaluate the feasibility of ultra-widefield (UWF) imaging to identify ocular pathologies amongst in- and out-patients in a tertiary university hospital. Methods: We followed a prospective double-blinded multicenter clinical study. In total, 634 patients from a university hospital with pulmonary, cardiovascular, and endocrine diseases were examined by two teams by conventional slit-lamp biomicroscopy (CBM). UWF images with Optos Tx200 were taken and subsequently graded independently by two retina specialists and graders from two reading centers for the presence of pre-defined pathologies. Interrater reliability was calculated using Fleiss statistical software. An independent, trained and certified ophthalmologist with retinal subspecialty (BL) classified all UWF images with retinal hemorrhages by severity and interrater agreement. Results: Complete data were available for 502 patients. The Moorfields Eye Hospital Reading Center, London, UK (RM), reported the highest number of cases with retinal pathologies (378), and the Reading Center GRADE Bonn, Germany (RB), did so for cases with optic disc cupping (466). Two retinal consultants (R1 and R2) from the Department of Ophthalmology, University Hospital Giessen and Marburg GmbH, Campus Giessen, Germany, noted optic disc pathologies. R1 reported 151 cases with optic disc pallor, while R2 reported only 39 disc pathologies. Both for clinical and for image readers, the early changes had equally low interrater reliability. The presence of at least 3 retinal hemorrhages had the highest interrater reliability (0.59). Conclusions: UWF imaging is convenient to identify overt retinal pathologies in patients at risk of ocular complications of their systemic disease who are attending hospital clinics. Imaging the eye allows for remote retinal assessment and for placing the patient into the appropriate clinical pathway for ophthalmology. Precis: UWF-imaging in a population of in- and out-patients at a university hospital who are at risk of retinal complications is effective to detect overt retinal pathologies and allows for tele-ophthalmology approaches to be enabled for placing the patients into the appropriate clinical pathways.

Effective Low-Cost Ophthalmological Screening With a Novel iPhone Fundus Camera at Community Centers.

Ophthalmologic care is inaccessible to many people due to a variety of factors, including the availability of providers, cost of equipment for ophthalmologic care, and transportation to clinics and appointments. Because many causes of blindness are both highly prevalent and preventable once identified, it is essential to address gaps in care for underserved populations. We developed a novel 3D-printed mobile retinal camera. In this study, we organized recurring student-run screening events around New York City that took place in community centers and churches, at which we utilized our device to take retinal images. Our screening events reached a diverse population of New Yorkers, disproportionately those with lower household income, many of whom had not had recent eye exams. To validate the device for use in telehealth ophthalmologic visits, we transmitted the images to a remote ophthalmologist for evaluation and compared the result with an on-site attending physician’s dilated eye exam. The subjective assessment indicated that 97% of images captured with the mobile retinal camera were acceptable for telehealth analysis. Remote image assessment by achieved 92% sensitivity and 83% specificity in detecting optic disc cupping, compared to the gold-standard on-site dilated eye exam. In addition, the device was portable, affordable, and able to be used by those with relatively little ophthalmologic training. We have demonstrated the utility of this affordable mobile retinal camera for telehealth ophthalmologic evaluation during community screening events that reached an underserved population to detect disease and connect with long-term care.