OBJECTIVE: The purpose of this study was to evaluate the amount of sensitivity and specificity of the slit-light (SL) method in the diagnosis of ocular cyclotorsion.
METHODS: One hundred and twenty eyes of 60 individuals (10-60 years old), with mean visual acuity of 0.08 ± 0.14 LogMAR, were divided into two groups (normal and torsion groups). Individuals without ocular motility disorder were selected as normal and patients with extraocular motility disorders and oblique muscle dysfunctions as the torsion group. The sensitivity and specificity of SL in the diagnosis of ocular torsion were measured by masked investigators and compared to fundus photography (FP). Inter- and intraobserver variability of these techniques was also determined.
RESULTS: The amounts of sensitivity and specificity of SL, measured by the first examiner, were 60% and 92% for intorsion and 50% and 96% for extorsion assessment, respectively. These amounts were 53% and 95% for intorsion, and 54% and 97% for extorsion by the second examiner. The contingency coefficient between the two examiners was 68.6% for SL. This amount was 61% between FP and SL for the first examiner and 63% for the second. The contingency coefficient for the repeatability of SL was 72.2% for the first examiner and 75.7% for the second. This amount was 71.2% between the two examiners.
CONCLUSIONS: SL can be considered a useful method for the diagnosis of cyclotorsion.

With plans for future long-duration crewed exploration, NASA has identified several high priority potential health risks to astronauts in space. One such risk is a collection of neurologic and ophthalmic findings termed spaceflight associated neuro-ocular syndrome (SANS). The findings of SANS include optic disc edema, globe flattening, retinal nerve fiber layer thickening, chorioretinal folds, hyperopic shifts, and cotton-wool spots. The cause of SANS was initially thought to be a cephalad fluid shift in microgravity leading to increased intracranial pressure, venous stasis and impaired CSF outflow, but the precise etiology of SANS remains ill defined. Recent studies have explored multiple possible pathogenic mechanisms for SANS including genetic and hormonal factors; a cephalad shift of fluid into the orbit and brain in microgravity; and disruption to the brain glymphatic system. Orbital, ocular, and cranial imaging, both on Earth and in space has been critical in the diagnosis and monitoring of SANS (e.g., fundus photography, optical coherence tomography (OCT), magnetic resonance imaging (MRI), and orbital/cranial ultrasound). In addition, we highlight near-infrared spectroscopy and diffusion tensor imaging, two newer modalities with potential use in future studies of SANS. In this manuscript we provide a review of these modalities, outline their current and potential use in space and on Earth, and review the reported major imaging findings in SANS.

UNASSIGNED: This study aimed to propose a new deep learning (DL) approach to automatically predict the retinal nerve fiber layer thickness (RNFLT) around optic disc regions in fundus photography trained by optical coherence tomography (OCT) and diagnose glaucoma based on the predicted comprehensive information about RNFLT.
UNASSIGNED: A total of 1403 pairs of fundus photographs and OCT RNFLT scans from 1403 eyes of 1196 participants were included. A residual deep neural network was trained to predict the RNFLT for each local image in a fundus photograph, and then a RNFLT report was generated based on the local images. Two indicators were designed based on the generated report. The support vector machines (SVM) algorithm was used to diagnose glaucoma based on the two indicators.
UNASSIGNED: A strong correlation was found between the predicted and actual RNFLT values on local images. On three testing datasets, we found the Pearson r to be 0.893, 0.850, and 0.831, respectively, and the mean absolute error of the prediction to be 14.345, 17.780, and 19.250 μm, respectively. The area under the receiver operating characteristic curves for discriminating glaucomatous from healthy eyes was 0.860 (95 % confidence interval, 0.799-0.921).
UNASSIGNED: We established a novel local image-based DL approach to provide comprehensive quantitative information on RNFLT in fundus photographs, which was used to diagnose glaucoma. In addition, training a deep neural network based on local images to predict objective detail information in fundus photographs provided a new paradigm for the diagnosis of ophthalmic diseases.

BACKGROUND: There is growing evidence supporting that vascular abnormalities contribute to multiple sclerosis (MS), and retinal microvasculature functions as a visible window to observe vessels. We hypothesized that retinal vascular curve tortuosity is associated with MS, which this study aims to address.
METHODS: Participants from the UK Biobank with complete clinical records and gradable fundus photos were included in the study. Arteriolar and venular curve tortuosity and vessel area density are quantified automatically using a deep learning system. Individuals with MS were matched to healthy controls using propensity score matching (PSM). Conditional logistic regression was used to investigate the association between retinal vascular characteristics and MS. We also used a receiver operating characteristic (ROC) curve to assess the diagnostic performance of MS.
RESULTS: Venular curve tortuosity (VCT) was found to be significantly associated with MS. And patients with multiple sclerosis were probable to have lower VCT than the non-MS group (OR = 0.22 [95 % CI, 0.05 to 0.92], P < 0.05).
CONCLUSIONS: Our study reveals a significant association between vessel curve tortuosity and MS. The lower curve tortuosity of the retinal venular network may indicate a higher risk of incident multiple sclerosis.

Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varying biomarkers, which may lead to unpredictable risk evaluations. This study introduces an automatic deep learning-based system for predicting stroke risk (both ischemic and hemorrhagic) and estimating the time frame of its occurrence, utilizing a comprehensive set of known retinal biomarkers from fundus images. Our system, tested on the UK Biobank and DRSSW datasets, achieved AUROC scores of 0.83 (95% CI: 0.79-0.85) and 0.93 (95% CI: 0.9-0.95), respectively. These results not only highlight our system\'s advantage over established benchmarks but also underscore the predictive power of retinal biomarkers in assessing stroke risk and the unique effectiveness of each biomarker. Additionally, the correlation between retinal biomarkers and cardiovascular diseases broadens the potential application of our system, making it a versatile tool for predicting a wide range of cardiovascular conditions.

OBJECTIVE: To investigate the utility of point of care screening of diabetic retinopathy (DR) and the impact of a telemedicine program to overcome current challenges.
METHODS: This was a retrospective study on people with type 2 diabetes mellitus (T2DM) who were screened for DR using the single-field non-mydriatic fundus photography at the point of care during routine follow-up visits at endocrinology clinic. Retinal images were uploaded and sent to a retina specialist for review. Reports indicating retinopathy status and the need for direct retinal examination were transmitted back to the endocrinology clinic. All patients were informed about DR status and, if needed, referred to the retina specialist for direct retinal examination.
RESULTS: Of the 1159 individuals screened for DR, 417 persons (35.98%) were screen-positive and referred to the retina specialist for direct retinal examination. A total of 121 individuals (29.01%) underwent direct retinal examination by the specialist. Diabetes macular edema (DME) was detected in 12.1%. In addition, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) were detected in 53.4% and 2.6% of the patients, respectively.
CONCLUSIONS: Integrating DR screening program at the point of care at the secondary care services improves the rate of DR screening as well as detection of sight threatening retinopathy and provides the opportunity for timely intervention in order to prevent advanced retinopathy in people with T2DM.

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.

UNASSIGNED: There is currently no tool available to assess the severity of damage in uveitis due to Behçet\'s syndrome (BS). In this preliminary study, we developed a new grading system to evaluate ocular damage and assessed it in a prospective cohort.
UNASSIGNED: A specialist in BS uveitis (YO) developed a grading system for ocular damage with five grades based on the extent of damage in the posterior segment. YO trained a senior and general ophthalmologist with sample fundus images. BS patients who had undergone color fundus photography during their routine visits in attack-free periods were included in the study. The color fundus photos of this prospective cohort were evaluated blindly by YO and his trainees using the new grading tool. Inter and intra-observer agreement between the graders were assessed by Cohen\'s kappa analysis. The evaluation of YO was considered as the gold standard.
UNASSIGNED: One hundred eighty-five eyes of 108 (29 F/79 M) patients with BS uveitis were graded for damage by two investigators. Their mean age was 38,58 years and their median ocular disease duration was 13 years. The gold standard and the two investigators exhibited substantial concordance with the ocular damage grading system. The inter- and intra-observer agreement were also almost perfect.
UNASSIGNED: The newly developed ocular damage grading system enables the standardization of damage severity in BS uveitis. It is imperative to conduct internal and external validations across diverse cohorts. Furthermore, future studies should investigate its correlation with other multimodal imaging methods such as fluorescein angiography and optical coherence tomography.

Ophthalmoscopy is a valuable tool in clinical practice. We report the use of a novel smartphone-based handheld device for visualisation and photo-documentation of the ocular fundus in veterinary medicine.
Selected veterinary patients of a referral ophthalmology service were included if one or both eyes had clear ocular media, allowing for examination of the fundus. Following pharmacological mydriasis, fundic images were obtained with a handheld fundus camera (Volk VistaView). For comparison, the fundus of a subset of animals was also imaged with a veterinary-specific fundus camera (Optomed Smartscope VET2).
The large field of view achieved by the Volk VistaView allowed for rapid and thorough observation of the ocular fundus in animals, providing a tool to visualise and record common pathologies of the posterior segment. Captured fundic images were sometimes overexposed, with the tapetal fundus artificially appearing hyperreflective when using the Volk VistaView camera, a finding that was less frequent when activating a \'veterinary mode\' that reduced the sensitivity of the camera\'s sensor. The Volk VistaView compared well with the Optomed Smartscope VET2.
The main study limitation was the small sample size.
The Volk VistaView camera was easy to use and provided good-quality fundic images in veterinary patients with healthy or diseased eyes, offering a wide field of view that was ideal for screening purposes.

The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While Leber\'s hereditary optic neuropathy (LHON) and optic neuritis (ON) are both clinically significant causes for optic atrophy, both relatively rare in the general population, contributing to limitations in obtaining large imaging datasets. This study therefore aims to develop a deep learning (DL) model based on small datasets that could distinguish the cause of optic disc atrophy using only fundus photography. We retrospectively reviewed fundus photographs of 120 normal eyes, 30 eyes (15 patients) with genetically-confirmed LHON, and 30 eyes (26 patients) with ON. Images were split into a training dataset and a test dataset and used for model training with ResNet-18. To visualize the critical regions in retinal photographs that are highly associated with disease prediction, Gradient-Weighted Class Activation Map (Grad-CAM) was used to generate image-level attention heat maps and to enhance the interpretability of the DL system. In the 3-class classification of normal, LHON, and ON, the area under the receiver operating characteristic curve (AUROC) was 1.0 for normal, 0.988 for LHON, and 0.990 for ON, clearly differentiating each class from the others with an overall total accuracy of 0.93. Specifically, when distinguishing between normal and disease cases, the precision, recall, and F1 scores were perfect at 1.0. Furthermore, in the differentiation of LHON from other conditions, ON from others, and between LHON and ON, we consistently observed precision, recall, and F1 scores of 0.8. The model performance was maintained until only 10% of the pixel values of the image, identified as important by Grad-CAM, were preserved and the rest were masked, followed by retraining and evaluation.