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Recent advances of artificial intelligence (AI) in retinal imaging found its application in two major categories: discriminative and generative AI. For discriminative tasks, conventional convolutional neural networks (CNNs) are still major AI techniques. Vision transformers (ViT), inspired by the transformer architecture in natural language processing, has emerged as useful techniques for discriminating retinal images. ViT can attain excellent results when pretrained at sufficient scale and transferred to specific tasks with fewer images, compared to conventional CNN. Many studies found better performance of ViT, compared to CNN, for common tasks such as diabetic retinopathy screening on color fundus photographs (CFP) and segmentation of retinal fluid on optical coherence tomography (OCT) images. Generative Adversarial Network (GAN) is the main AI technique in generative AI in retinal imaging. Novel images generated by GAN can be applied for training AI models in imbalanced or inadequate datasets. Foundation models are also recent advances in retinal imaging. They are pretrained with huge datasets, such as millions of CFP and OCT images and fine-tuned for downstream tasks with much smaller datasets. A foundation model, RETFound, which was self-supervised and found to discriminate many eye and systemic diseases better than supervised models. Large language models are foundation models that may be applied for text-related tasks, like reports of retinal angiography. Whereas AI technology moves forward fast, real-world use of AI models moves slowly, making the gap between development and deployment even wider. Strong evidence showing AI models can prevent visual loss may be required to close this gap.

Wide field retinal imaging has emerged as a transformative technology over the last few decades, revolutionizing our ability to visualize the intricate landscape of the retina. By capturing expansive retinal areas, these techniques offer a panoramic view going beyond traditional imaging methods. In this review, we explore the significance of retinal imaging-based biomarkers to help diagnose ocular and systemic conditions. We discuss quantitative biomarkers, including ischemic index, nonperfusion area and more, and their application in diabetic retinopathy, central retinal vein occlusion, neurodegenerative diseases, and more. In addition, we outline qualitative biomarkers such as choroidal venous hyperpermeability and intervortex anastomoses. The role of wide field fundus autofluorescence in assessing hereditary retinal diseases is also emphasized. Standardized imaging procedures, professional collaboration, and validation across a range of clinical circumstances are necessary for the effective use of these biomarkers. They have the potential to transform disease identification, risk assessment, and customize therapy.

Abstract The use of deep learning for OCT image classification could enhance the diagnosis and monitoring of retinal diseases. However, challenges like variability in retinal abnormalities, noise, and artifacts in OCT images limit its clinical use. Our study aimed to evaluate the performance of various deep learning (DL) architectures in classifying retinal pathologies versus healthy cases based on OCT images, under data scarcity and label noise. We examined five DL architectures: ResNet18, ResNet34, ResNet50, VGG16, and InceptionV3. Fine-tuning of the pre-trained models was conducted on 5526 OCT images and reduced subsets down to 21 images to evaluate performance under data scarcity. The performance of models fine-tuned on subsets with label noise levels of 10%, 15%, and 20% was evaluated. All DL architectures achieved high classification accuracy (> 90%) with training sets of 345 or more images. InceptionV3 achieved the highest classification accuracy (99%) when trained on the entire training set. However, classification accuracy decreased and variability increased as sample size decreased. Label noise significantly affected model accuracy. Compensating for labeling errors of 10%, 15%, and 20% requires approximately 4, 9, and 14 times more images in the training set to reach the performance of 345 correctly labeled images. The results showed that DL models fine-tuned on sets of 345 or more OCT images can accurately classify retinal pathologies versus healthy controls. Our findings highlight that while mislabeling errors significantly impact classification performance in OCT analysis, this can be effectively mitigated by increasing the training sample size. By addressing data scarcity and labeling errors, our research aims to improve the real-world application and accuracy of retinal disease management.

Abstract Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In ophthalmology, large multi-modal datasets are abundantly available and conveniently accessible as modern retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography (OCT) scans to assess the eye. In this context, we introduce a novel multi-modal contrastive learning-based pipeline to facilitate learning joint representations for the two retinal imaging modalities. After self-supervised pre-training on 153,306 scan pairs, we show that such a pre-training framework can provide both a retrieval system and encoders that produce comprehensive OCT and fundus image representations that generalize well for various downstream tasks on three independent external datasets, explicitly focusing on clinically pertinent prediction tasks. In addition, we show that interchanging OCT with lower-cost fundus imaging can preserve the predictive power of the trained models.

Abstract While Alzheimer’s disease and other neurodegenerative diseases have traditionally been viewed as brain disorders, there is growing evidence indicating their manifestation in the eyes as well. The retina, being a developmental extension of the brain, represents the only part of the central nervous system that can be noninvasively imaged at a high spatial resolution. The discovery of the specific pathological hallmarks of Alzheimer’s disease in the retina of patients holds great promise for disease diagnosis and monitoring, particularly in the early stages where disease progression can potentially be slowed. Among various retinal imaging methods, hyperspectral imaging has garnered significant attention in this field. It offers a label-free approach to detect disease biomarkers, making it especially valuable for large-scale population screening efforts. In this review, we discuss recent advances in the field and outline the current bottlenecks and enabling technologies that could propel this field toward clinical translation.

Abstract Purpose To evaluate the baseline characteristics of fundus autofluorescence (FAF) in patients with submacular hemorrhage (SMH). Methods This retrospective study included patients diagnosed with treatment-naive, foveal-involving subretinal hemorrhage (size > 2-disc diameters) of any etiology, presenting between June 2017 and June 2023. Only cases with good-quality color fundus photographs, optical coherence tomography (OCT) scans, and blue-light FAF images at baseline were included. SMH imaging characteristics were documented and correlated with treatment outcomes. A successful treatment outcome was defined as the reduction, displacement or clearance of the SMH from beneath the fovea. Results Nineteen cases of SMH (13 males, 6 females), ranging from 14 to 85 years, were analyzed. Neovascular age-related macular degeneration (nAMD) was the most common etiology (n = 11, 58%). Baseline visual acuity ranged from 6/9 to counting fingers at ½ meter, with a median presentation time of 7 days from symptom onset (range: 1–57 days). Treatment success was observed in 13 eyes (68%). Hypoautofluoroscence on FAF was significantly associated with SMH resolution (p = 0.021). However, no association was found between treatment success and clinical hemorrhage characteristics (p = 0.222), OCT findings (p = 0.222), or specific treatments (p > 0.05). Hypoautofluoroscence on FAF was the sole predictor of treatment success, as demonstrated by Spearman’s correlation (r = 0.637; p = 0.003) and linear regression analysis (p = 0.003). Conclusion FAF, in conjunction with color fundus photography and OCT, may provide valuable insights for clinicians in formulating treatment strategies for patients with SMH. Hypoautofluoroscence on FAF was a significant predictor of successful SMH resolution in this study.

Abstract Image based cell-specific biomarkers will play an important role in monitoring treatment outcomes of novel therapies in patients with Stargardt (STGD1) disease and may provide information on the exact mechanism of retinal degeneration. This study reports retinal image features from conventional clinical imaging and from corresponding high-resolution imaging with a confocal adaptive optics scanning laser ophthalmoscope (AOSLO) in a heterogenous cohort of patients with Stargardt (STGD1) disease. This is a prospective observational study in which 16 participants with clinically and molecularly confirmed STGD1, and 7 healthy controls underwent clinical assessment and confocal AOSLO imaging. Clinical assessment included short-wavelength and near-infrared fundus autofluorescence, spectral-domain optical coherence tomography, and macular microperimetry. AOSLO images were acquired over a range of retinal eccentricities (0°–20°) and mapped to areas of interest from the clinical images. A regular photoreceptor mosaic was identified in areas of normal or near normal retinal structure on clinical images. Where clinical imaging indicated areas of retinal degeneration, the photoreceptor mosaic was disorganised and lacked unambiguous cones. Discrete hyper-reflective foci were identified in 9 participants with STGD1 within areas of retinal degeneration. A continuous RPE cell mosaic at the fovea was identified in one participant with an optical gap phenotype. The clinical heterogeneity observed in STGD1 is reflected in the findings on confocal AOSLO imaging.

Abstract Background Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown. Methods This was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set. Results In an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74–0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86–1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73–1.00). Conclusions AI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk. Trial registration This is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413). Graphical Abstract

Abstract Background Alzheimer’s disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups. Methods The BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort (n = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort (n = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aβ-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments. Results We aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aβ- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding. Conclusions We envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection. Trial registration The BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23–09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC.

Abstract Age-related macular degeneration (AMD) is a leading cause of blindness. It is associated with peripheral drusen which has not been categorized. We investigated peripheral drusen to validate an image grading system and to understand possible associations between peripheral drusen and AMD. We collated clinical data, ultra-widefield (UWF) pseudocolor fundus images and Spectral-Domain Optical Coherence Tomography (SD-OCT) scans from consecutive retinal patients. SD-OCT scans were used to determine AMD stage. A masked retinal specialist recorded the types of peripheral drusen observed in UWF images. Eyes whose UWF images did not pass quality screening and those without AMD and peripheral drusen were excluded from the study. Statistical tests were utilized to determine the validity of our grading system and associations of peripheral drusen with AMD. A total of 481 eyes (283 subjects) were included in the study (mean age 73.1 ± 1.2years, 64.3% female). Interobserver and test–retest statistical analyses to evaluate the UWF image grading system resulted in Cohen’s Kappa 0.649 (p

Abstract This commentary article delves into the transformative role of optical coherence tomography angiography (OCTA) in diagnosing and managing a wide array of eye conditions, including diabetic retinopathy, age-related macular degeneration, retinal vein occlusions, and white dot syndromes. Developed in 2005, OCTA has emerged as a non-invasive, high-resolution imaging technique that offers advantages over traditional fluorescein angiography (FA), providing quicker and safer monitoring of ocular conditions with similar diagnostic accuracy. In diabetic retinopathy, OCTA has been instrumental in early identification of retinal changes, offering quantifiable metrics including perfused capillary density (PCD) for assessing vascular alterations. For age-related macular degeneration (AMD), OCTA has deepened our understanding of non-exudative neovascular AMD, allowing for more effective monitoring and potential earlier initiation of treatment. In cases of retinal vein occlusions, OCTA can reveal specific microvascular features and allow for depth-resolved measurements of the foveal avascular zone, providing significant prognostic implications. OCTA has also been invaluable in studying rare white dot syndromes, enabling nuanced differentiation between conditions that often present similarly. Emerging research also suggests that OCTA can have potential utility in neurodegenerative diseases like Alzheimer’s, where retinal vascular patterns could offer diagnostic insights. While OCTA is revolutionizing ophthalmic care, further clinical trials and standardization are needed for its broader adoption into clinical practice.

Abstract Background Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. Main text This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. Conclusion AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.

Abstract Background Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina’s unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. Main text This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson’s disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. Conclusion AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina’s unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Glaucoma is a leading cause of irreversible blindness worldwide, with its pathophysiology remaining inadequately understood. Among the various proposed theories, the vascular theory, suggesting a crucial role of retinal vasculature deterioration in glaucoma onset and progression, has gained significant attention. Traditional imaging techniques, such as fundus fluorescein angiography, are limited by their invasive nature, time consumption, and qualitative output, which restrict their efficacy in detailed retinal vessel examination. Optical coherence tomography angiography (OCTA) emerges as a revolutionary imaging modality, offering non-invasive, detailed visualization of the retinal and optic nerve head microvasculature, thereby marking a significant advancement in glaucoma diagnostics and management. Since its introduction, OCTA has been extensively utilized for retinal vasculature imaging, underscoring its potential to enhance our understanding of glaucoma’s pathophysiology, improving diagnosis, and monitoring disease progression. This review aims to summarize the current knowledge regarding the role of OCTA in glaucoma, particularly its potential applications in diagnosing, monitoring, and understanding the pathophysiology of the disease. Parameters pertinent to glaucoma will be elucidated to illustrate the utility of OCTA as a tool to guide glaucoma management.

Background Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.

Abstract Background Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME. Methods A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated. Results The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p

Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features. However, manual interpretation of OCT scans is labor-intensive and prone to variability, often leading to diagnostic inconsistencies. To address this, we leveraged the RETFound model, a foundation model pretrained on 1.6 million unlabeled retinal OCT images, to automate the classification of key disease signatures on OCT. We finetuned RETFound and compared its performance with the widely used ResNet-50 model, using single-task and multitask modes. The dataset included 1770 labeled B-scans with various disease features, including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, and pigment epithelial detachment (PED). The performance was evaluated using accuracy and AUC-ROC values, which ranged across models from 0.75 to 0.77 and 0.75 to 0.80, respectively. RETFound models display comparable specificity and sensitivity to ResNet-50 models overall, making it also a promising tool for retinal disease diagnosis. These findings suggest that RETFound may offer improved diagnostic accuracy and interpretability for specific tasks, potentially aiding clinicians in more efficient and reliable OCT image analysis.

PurposeTo compare radial peripapillary capillary (RPC) vascular plexus parameters and peripapillary retinal nerve fiber layer (pRNFL) thickness between Early-Treated Adults with Phenylketonuria (ETPKU) and controls.MethodsThis observational study was a monocentric, case control study including 36 eyes of 36 participants. Among these, 18 were early-treated PKU (ETPKU) and 18 were controls. A SD-OCTA (XR Avanti AngioVue OCTA; Optovue Inc., Fremont, CA) was employed to assess the OCT and OCTA parameters of all the participants. The main outcome measures were the RPC vessels density (VD) %, and the pRNFL thickness.ResultsThe average pRNFL thickness was significantly reduced in ETPKU (110.78 ± 12.48 μm) compared to controls (113.22 ± 13.95 μm), p = 0.046. The mean VD% of the small vessels of the RPC plexus was 52.31 ± 2.2 in ETPKU and 50.71 ± 3.2 in controls (p = 0.049), while the VD% of all the radial peripapillary capillary plexus (RPCP) was 58.5 ± 2.2 in ETPKU and 55.08 ± 3.4 in controls (p

Purpose: The purpose of this study was to compare diabetic retinopathy (DR) severity levels assessed from 7 standard-field stereoscopic color photographs on a 35° fundus camera to both Clarus and Optos ultrawidefield color images. Design: Cross-sectional, comparative imaging study. Participants: Participants with DR imaged at a single-center retina practice. Methods: Participants were imaged on 3 cameras at a single visit with the Topcon 35° fundus camera, Clarus, and Optos. The DR Severity Scale (DRSS) level was determined within the 7-field (7F) area of each image set using the ETDRS scale. An additional global DRSS was assigned for both Clarus and Optos images using the entire visible retina. Weighted kappa (wκ) measured the agreement between cameras. Main Outcome Measures: The primary outcome was a 3-way comparison of DRSS level within the 7F area imaged on the 3 cameras. Secondary outcomes included a comparison of the DRSS obtained with standard 7F imaging to the global DRSS of Clarus and Optos and a comparison of the global DRSS between Clarus and Optos only. Results: Ninety-seven eyes (50 participants) were evaluated. Agreement within 1-step of ETDRS levels between standard 7F imaging and Clarus 7F was 90.1% (wκ = 0.65), and with Optos 7F in 85.9%, (wκ = 0.58). Agreement within 1-step between standard 7F imaging and Clarus global was 88.9% of eyes (wκ = 0.63), and Optos global was 85.7%, (wκ = 0.54). Agreement between Clarus and Optos global DR level within 1-step was 89.1% (wκ = 0.68). Intergrader agreement for the 7F ETDRS level was 96% for standard 7F imaging, 98% for Clarus, and 95.5% for Optos. Conclusions: These findings suggest that when evaluating the 7F area on Clarus and Optos, DR severity grades are comparable to standard 7F imaging. However, it is important to understand the unique attributes and differences of each fundus camera when changing the type of system used in a clinical setting due to upgrading equipment. Additionally, if the facility has access to > 1 device, there should not be an exchange between cameras for the same patient. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Purpose: To evaluate mydriatic handheld retinal imaging performance assessed by point-of-care (POC) artificial intelligence (AI) as compared with retinal image graders at a centralized reading center (RC) in identifying diabetic retinopathy (DR) and diabetic macular edema (DME). Design: Prospective, comparative study. Subjects: Five thousand five hundred eighty-five eyes from 2793 adult patients with diabetes. Methods: Point-of-care AI assessment of disc and macular handheld retinal images was compared with RC evaluation of validated 5-field handheld retinal images (disc, macula, superior, inferior, and temporal) in identifying referable DR (refDR; defined as moderate nonproliferative DR [NPDR], or worse, or any level of DME) and vision-threatening DR (vtDR; defined as severe NPDR or worse, or any level of center-involving DME [ciDME]). Reading center evaluation of the 5-field images followed the international DR/DME classification. Sensitivity (SN) and specificity (SP) for ungradable images, refDR, and vtDR were calculated. Main Outcome Measures: Agreement for DR and DME; SN and SP for refDR, vtDR, and ungradable images. Results: Diabetic retinopathy severity by RC evaluation: no DR, 67.3%; mild NPDR, 9.7%; moderate NPDR, 8.6%; severe NPDR, 4.8%; proliferative DR, 3.8%; and ungradable, 5.8%. Diabetic macular edema severity by RC evaluation was as follows: no DME (80.4%), non–ciDME (7.7%), ciDME (4.4%), and ungradable (7.5%). Referable DR was present in 25.3% and vtDR was present in 17.5% of eyes. Images were ungradable for DR or DME in 7.5% by RC evaluation and 15.4% by AI. There was substantial agreement between AI and RC for refDR (κ = 0.66) and moderate agreement for vtDR (κ = 0.54). The SN/SP of AI grading compared with RC evaluation was 0.86/0.86 for refDR and 0.92/0.80 for vtDR. Conclusions: This study demonstrates that POC AI following a defined handheld retinal imaging protocol at the time of imaging has SN and SP for refDR that meets the current United States Food and Drug Administration thresholds of 85% and 82.5%, but not for vtDR. Integrating AI at the POC could substantially reduce centralized RC burden and speed information delivery to the patient, allowing more prompt eye care referral. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Abstract Background Diabetic retinopathy (DR) is a leading cause of blindness and involves retinal capillary damage, microaneurysms, and altered blood flow regulation. Optical coherence tomography angiography (OCTA) is a non-invasive way of visualizing retinal vasculature but has not been used extensively to study blood flow heterogeneity. The purpose of this study is to detect and quantify blood flow heterogeneity utilizing en-face swept source OCTA in patients with DR. Methods This is a prospective clinical study which examined patients with either type 1 or 2 diabetes mellitus. Each included eye was graded clinically as no DR, mild DR, or moderate-severe DR. Ten consecutive en face 6 × 6 mm foveal SS-OCTA images were obtained from each eye using a PLEX Elite 9000 (Zeiss Meditec, Dublin, CA). Built-in fixation-tracking, follow-up functions were utilized to reduce motion artifacts and ensure same location imaging in sequential frames. Images of the superficial and deep vascular complexes (SVC and DVC) were arranged in temporal stacks of 10 and registered to a reference frame for segmentation using a deep neural network. The vessel segmentation was then masked onto each stack to calculate the pixel intensity coefficient of variance (PICoV) and map the spatiotemporal perfusion heterogeneity of each stack. Results Twenty-nine eyes were included: 7 controls, 7 diabetics with no DR, 8 mild DR, and 7 moderate-severe DR. The PICoV correlated significantly and positively with DR severity. In patients with DR, the perfusion heterogeneity was higher in the temporal half of the macula, particularly in areas of capillary dropout. PICoV also correlates as expected with the established OCTA metrics of perfusion density and vessel density. Conclusion PICoV is a novel way to analyze OCTA imaging and quantify perfusion heterogeneity. Retinal capillary perfusion heterogeneity in both the SVC and DVC increased with DR severity. This may be related to the loss of retinal capillary perfusion autoregulation in diabetic retinopathy.

Adaptive optics(AO)is a technique used to optimize the functionality of optical systems through the reduction of wavefront distortion and optical aberrations. AO-based retinal imaging reduces the occurrence of optical aberrations in the refractive system, consequently improving the resolution and overall quality of retinal imaging. As a result, AO-based retinal imaging has potential wide-ranging application in ophthalmology. A combination of AO, fundus camera, scanning laser ophthalmoscope, optical coherence tomography technique and optical coherence tomography angiography, can be applied to observe the distribution, morphology and function of retinal cone cells in the healthy retinal, and to comprehend the shape and perfusion of retinal vessels in the fine vascular layer. With this technique, it can also possible to perform qualitative and quantitative analysis of the number and shape of cells in the ocular fundus, the cribriform plate, and the microscopic structures of the retinal microvascular system and nerve tissue. It can be expected as a novel tool for the early diagnosis, follow-up of therapy effects, and identification of progression of ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration, glaucoma and hereditary retinal diseases.

Diabetes mellitus (DM) significantly impacts ocular health, particularly the retina and choroid, leading to diabetic retinopathy (DR) and diabetic macular edema (DME). This study aimed to evaluate the effects of intravitreal dexamethasone (IVD) on the choroidal vascularity index (CVI) in order to evaluate its potential as a biomarker. This was a retrospective observational study in which 37 eyes with DME treated with IVD were included. Baseline and follow-up (2 and 4 months post-injection) comprehensive ophthalmological examinations and spectral domain optical coherence tomography (SD-OCT) were performed. The CVI, luminal area (LA), stromal area (SA), and total area (TA) were assessed. No statistically significant changes in CVI, LA, SA, and TA were observed in the overall patient sample from baseline to 2 and 4 months post-IVD injection. However, a significant CVI reduction was noted in responder patients at 4 months. Positive correlations were found between the CVI and central macular thickness (CMT) at 2 months, suggesting that higher CMT values are associated with higher CVI values. This study indicated that IVD influences choroidal circulation, as evidenced by CVI reduction in responder patients. The CVI could potentially serve as an objective biomarker for treatment response in DME. Larger, prospective studies are warranted to clarify these findings further.

Background: Age-related macular degeneration (AMD) is a complex eye disorder with an environmental and genetic origin, affecting millions worldwide. The study aims to explore the association between retinal morphology and the polygenic risk score (PRS) for AMD using fundus images and deep learning techniques. Methods: The study used and pre-processed 23,654 fundus images from 332 subjects (235 patients with AMD and 97 controls), ultimately selecting 558 high-quality images for analysis. The fine-tuned DenseNet121 deep learning model was employed to estimate PRS from single fundus images. After training, deep features were extracted, fused, and used in machine learning regression models to estimate PRS for each subject. The Grad-CAM technique was applied to examine the relationship between areas of increased model activity and the retina’s morphological features specific to AMD. Results: Using the hybrid approach improved the results obtained by DenseNet121 in 5-fold cross-validation. The final evaluation metrics for all predictions from the best model from each fold are MAE = 0.74, MSE = 0.85, RMSE = 0.92, R2 = 0.18, MAPE = 2.41. Grad-CAM heatmap evaluation showed that the model decisions rely on lesion area, focusing mostly on the presence of drusen. The proposed approach was also shown to be sensitive to artifacts present in the image. Conclusions: The findings indicate an association between fundus images and AMD PRS, suggesting that deep learning models may effectively estimate genetic risk for AMD from retinal images, potentially aiding in early detection and personalized treatment strategies.

In recent years, optoretinography has become an important functional imaging method for the retina, as light-evoked changes in the photoreceptors have been demonstrated for a large number of different OCT systems. Full-field swept-source optical coherence tomography (FF-SS-OCT) is particularly phase-stable, and it is currently the only technique sensitive enough to detect the smaller functional changes in the inner plexiform layer (IPL). However, the resolution of state-of-the art FF-SS-OCT systems is not high enough to distinguish individual photoreceptors. This makes it difficult to separate rods from cones. In this work, we circumvent this problem by separating the functional changes in rods and cones by their different temporal dynamics to the same light stimulus. For this purpose, a mathematical model was developed that represents the measured signals as a superposition of two impulse responses. The developed model describes the measured data under different imaging conditions very well and is able to analyze the sensitivity and temporal dynamics of the two photoreceptor types separately.

Khalid Mohammad Alabdulwahhab Department of Ophthalmology, College of Medicine, Majmaah University, Majmaah, Kingdom of Saudi ArabiaCorrespondence: Khalid Mohammad Alabdulwahhab, Department of Ophthalmology, College of Medicine, Majmaah University, Majmaah, Kingdom of Saudi Arabia, Email k.alabdulwahhab@mu.edu.saBackground: Screening of diabetic retinopathy (DR) using the current digital imaging facilities in a primary health care setting is still in its early stages in Saudi Arabia. This study aims to reduce the risk of vision impairment and blindness among known diabetic people through early identification by general practitioners (GP) in a primary health care setting in Saudi Arabia. The objective of this study was to evaluate the accuracy of diabetic retinopathy (DR) detection by general practitioners (GPs) by comparing the agreement of DR assessment between GPs and ophthalmologists’ assessment as a gold standard.Methods: A hospital-based, six-month cross-sectional study was conducted, and the participants were type 2 diabetic adults from the diabetic registries of seven rural PHCs, in Saudi Arabia. After medical examination, the participants were then evaluated by fundus photography using a non-mydriatic fundus camera without medication for mydriasis. Presence or absence of DR was graded by the trained GPs in the PHCs and then compared with the grading of an ophthalmologist which was taken as a reference or a gold standard.Results: A total of 899 diabetic patients were included, and the mean age of the patients was 64.89 ± 11.01 years. The evaluation by the GPs had a sensitivity of 80.69 [95% CI 74.8– 85.4]; specificity of 92.23 [88.7– 96.3]; positive predictive value, 74.1 [70.4– 77.0]; negative predictive value, 73.34 [70.6– 77.9]; and an accuracy of 84.57 [81.8– 89.88]. For the consensus of agreement the adjusted kappa coefficient was from 0.74 to 0.92 for the DR.Conclusion: This study demonstrates that trained GPs in rural health centers are able to provide reliable detection results of DR from fundus photographs. The study highlights the need for early DR screening programs in the rural areas of Saudi Arabia to facilitate early identification of the condition and to lessen impact of blindness due to diabetes.Keywords: diabetic retinopathy, rural health, primary health care, diabetes mellitus, non mydriatic camera, retinal imaging, fundus photography, primary health care, diabetes, family health, ophthalmology, prevention, screening

AIM: To measure the difference of intraoperative central macular thickness (CMT) before, during, and after membrane peeling and investigate the influence of intraoperative macular stretching on postoperative best corrected visual acuity (BCVA) outcome and postoperative CMT development. METHODS: A total of 59 eyes of 59 patients who underwent vitreoretinal surgery for epiretinal membrane was analyzed. Videos with intraoperative optical coherence tomography (OCT) were recorded. Difference of intraoperative CMT before, during, and after peeling was measured. Pre- and postoperatively obtained BCVA and spectral-domain OCT images were analyzed. RESULTS: Mean age of the patients was 70±8.13y (range 46-86y). Mean baseline BCVA was 0.49±0.27 logMAR (range 0.1-1.3). Three and six months postoperatively the mean BCVA was 0.36±0.25 (P=0.01 vs baseline) and 0.38±0.35 (P=0.08 vs baseline) logMAR respectively. Mean stretch of the macula during surgery was 29% from baseline (range 2%-159%). Intraoperative findings of macular stretching did not correlate with visual acuity outcome within 6mo after surgery (r=-0.06, P=0.72). However, extent of macular stretching during surgery significantly correlated with less reduction of CMT at the fovea centralis (r=-0.43, P

(1) Background: The fundus examination is one of the best and popular methods in the assessment of vascular status in the human body. Direct viewing of retinal vessels by ophthalmoscopy has been utilized in judging hypertensive change or arteriosclerosis. Recently, fundus imaging with the non-mydriatic scanning laser ophthalmoscope (SLO) has been widely used in ophthalmological clinics since it has multimodal functions for optical coherence tomography or angiography with contrast agent dye. The purpose of this study was to examine the utility in detecting arteriosclerosis of retinal vessels in SLO images; (2) Methods: Both color and blue standard field SLO images of eyes with diabetic retinopathy (DR) were examined retrospectively. Retinal arteriosclerosis in color SLO images was graded according to the Scheie classification. Additionally, characteristics of retinal arterioles in blue SLO images were identified and examined for their relevance to arteriosclerosis grades, stages of DR or general complications; (3) Results: Relative to color fundus images, blue SLO images showed distinct hyper-reflective retinal arterioles against a monotone background. Irregularities of retinal arterioles identified in blue SLO images were frequently observed in the eyes of patients with severe arteriosclerosis (Grade 3: 79.0% and Grade 4: 81.8%). Furthermore, the findings on arterioles were more frequently associated with the eyes of DR patients with renal dysfunction (p < 0.05); (4) Conclusions: While color SLO images are equally as useful in assessing retinal arteriosclerosis as photography or ophthalmoscopy, the corresponding blue SLO images show arteriosclerotic lesions with high contrast in a monotone background. Retinal arteriosclerosis in eyes of advanced grades or advanced DR frequently show irregularities of retinal arterioles in the blue images. The findings of low, uneven, or discontinuous attenuation were easier to find in blue than in color SLO images. Consequently, blue SLO images can show pathological micro-sclerosis in retinal arterioles and are potentially one of the safe and practical methods for the vascular assessment of diabetic patients.

Stargardt disease (STGD1), associated with biallelic variants in the ABCA4 gene, is the most common heritable macular dystrophy and is currently untreatable. To identify potential treatment targets, we characterized surviving STGD1 photoreceptors. We used clinical data to identify macular regions with surviving STGD1 photoreceptors. We compared the hyperreflective bands in the optical coherence tomographic (OCT) images that correspond to structures in the STGD1 photoreceptor inner segments to those in controls. We used adaptive optics scanning light ophthalmoscopy (AO-SLO) to study the distribution of cones and AO-OCT to evaluate the interface of photoreceptors and retinal pigment epithelium (RPE). We found that the profile of the hyperreflective bands differed dramatically between patients with STGD1 and controls. AO-SLOs showed patches in which cone densities were similar to those in healthy retinas and others in which the cone population was sparse. In regions replete with cones, there was no debris at the photoreceptor-RPE interface. In regions with sparse cones, there was abundant debris. Our results raise the possibility that pharmaceutical means may protect surviving photoreceptors and so mitigate vision loss in patients with STGD1.

(1) Background: Previously, VESsel GENeration (VESGEN) software was used to map and quantify vascular changes observed on fluorescein angiography (FA) in subjects (n = 15 eyes) with retinal pathology ranging from mild non-proliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR). In the current study, we used VESGEN for the assessment of individuals with early-stage NPDR imaged by FA (Cohort 1) and by optical coherence tomography angiography (OCTA; Cohort 2). (2) Methods: Cohort 1 included type 2 diabetics (T2D), represented 21 eyes (ranging from no DR to moderate DR), and also included nondiabetic controls (NDC; n = 15 eyes). Cohort 2 consisted of 23 eyes from T2D subjects (including no DR subjects and moderate DR subjects) and NDC (n = 18 eyes). (3) Results: In the FA-VESGEN study, total tortuosity (Tv) of microvessels (G ≥ 6) increased in T2D with mild DR compared to the controls. In contrast, the VESGEN analysis of OCTA images showed that vessel length (characterized as density) was lower in T2D subjects before the diagnosis of DR and following the diagnosis of DR when compared to the controls. Additionally, T2D showed a significant decrease in vessel area (density). (4) Conclusions: FA elucidated the vessel morphology of small-generation microvessels to a greater degree than OCTA; however, OCTA identified changes in vessel density better than FA. VESGEN analysis can be used with both standard FA and OCTA to facilitate our understanding of early events in DR, including before the clinical diagnosis of DR.

Adaptive optics (AO) has been used in many applications, including astronomy, microscopy, and medical imaging. In retinal imaging, AO provides real-time correction of the aberrations introduced by the cornea and the lens to facilitate diffraction-limited imaging of retinal microstructures. Most importantly, AO-based retinal imagers provide cellular-level resolution and quantification of changes induced by retinal diseases and systemic diseases that manifest in the eye enabling disease diagnosis and monitoring of disease progression or the efficacy of treatments. In this paper, we present an overview of our team efforts over almost two decades to develop high-resolution retinal imagers suitable for clinical use. Several different types of imagers for human and small animal eye imaging are reviewed, and representative results from multiple studies using these instruments are shown. These examples demonstrate the extraordinary power of AO-based retinal imaging to reveal intricate details of morphological and functional characteristics of the retina and to help elucidate important aspects of vision and of the disruptions that affect delicate retinal tissue.

Abstract Background Migraine is a common and distressing neurological condition characterised by recurrent throbbing headaches, nausea and heightened sensitivity to light and sound. Accumulating evidence suggests that cerebral arteries dilate during migraine, causing distal microvessels to constrict, which could activate nociceptors and cause onset of headache pain. If so, preventing or attenuating chronic microvascular constriction, and promoting a dilatory phenotype, may reduce frequency and/or severity of migraines. The primary aim of the L-Arginine and Aged Garlic Extract (LARGE) trial is to investigate whether oral treatment with dietary nutraceuticals, L-arginine and aged garlic extract (AGE), both systemic vasodilatory agents, will alleviate migraine frequency, duration and severity in adults with chronic frequent episodic migraines. Methods The study is a randomised double-blind placebo-controlled phase-II single-site clinical trial conducted in Perth, Australia. The target sample is to recruit 240 participants diagnosed with chronic frequent episodic migraines between 18 and 80 years of age. Participants will be randomised to one of four treatment groups for 14 weeks (placebo induction for 2 weeks, followed by 12 weeks on one of the respective treatment arms): placebo, L-arginine, AGE, or a combination of L-arginine and AGE. The doses of L-arginine and AGE are 1.5 g and 1 g daily, respectively. The primary outcome is to assess migraine response using change in migraine frequency and intensity between baseline and 12 weeks. Secondary outcomes include the impact of L-arginine and/or AGE on photosensitivity, retinal vessel changes, and blood biomarker concentrations of vascular tone, following a 12-week intervention. Discussion The protocol describes the oral administration of 2 nutraceutical-based interventions as possible prophylactic treatments for chronic frequent episodic migraines, with potential for direct clinical translation of outcomes. Potential limitations of the study include the fixed-dose design of each treatment arm and that in vivo neuroimaging methods, such as magnetic resonance imaging (MRI), will not be conducted to determine putative cerebro-vasodilatory changes to coincide with the outcome measures. Dose-response studies may be indicated. Trial registration The trial was retrospectively registered with the Australian New Zealand Clinical Trials Registry ACTRN12621001476820 (Universal Trial Number: U1111-1268-1117) on 04/08/2021. This is protocol version 1, submitted on 25/11/2022.

Abstract Background Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5–10% (termed as borderline-QRISK3 group) using the UK Biobank. Methods Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups. Results Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30–1.52) with a 13.1% (95% CI, 11.7–14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010–0.017) in non-statin cohort, 0.013 (0.007–0.019) in stage 1 hypertension cohort, and 0.023 (0.018–0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3. Conclusions Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD.

Optical Coherence Tomography (OCT) is a widely used 3D imaging technology in ophthalmology. Segmentation of retinal layers in OCT is important for diagnosis and evaluation of various retinal and systemic diseases. While 2D segmentation algorithms have been developed, they do not fully utilize contextual information and suffer from inconsistency in 3D. We propose neural networks to combine motion correction and segmentation in 3D. The proposed segmentation network utilizes 3D convolution and a novel graph pyramid structure with graph-inspired building blocks. We also collected one of the largest OCT segmentation dataset with manually corrected segmentation covering both normal examples and various diseases. The experimental results on three datasets with multiple instruments and various diseases show the proposed method can achieve improved segmentation accuracy compared with commercial softwares and conventional or deep learning methods in literature. Specifically, the proposed method reduced the average error from 38.47% to 11.43% compared to clinically available commercial software for severe deformations caused by diseases. The diagnosis and evaluation of diseases with large deformation such as DME, wet AMD and CRVO would greatly benefit from the improved accuracy, which impacts tens of millions of patients.

Abstract Background Bietti crystalline dystrophy is primarily a retinal dystrophy caused by a CYP4V2 mutation and typically presents with crystalline retinal deposits in the posterior fundus. Case presentation We present the case of an otherwise healthy 39-year-old Iranian woman with no family history of ocular disease who suffered with progressive vision loss that had started 2 years prior to presentation. Ocular examination revealed blurry optic nerve head margin and diffuse retinal crystalline deposit in both eyes. Spectral domain optical coherence tomography images showed retinal crystals, located mostly in outer retinal layers, with some areas of outer retinal tubulation and attenuation of outer retinal layers. Crystalline deposits were better visualized on near-infrared images as hyperreflective spots. Fundus autofluorescence images showed hyperautofluorescence areas on optic nerve head consistent with optic nerve head drusen and large hypoautofluorescence areas in posterior retina consistent with retinal pigment epithelium atrophy. Cystinosis was ruled out by blood testing. Conclusion Bietti crystalline dystrophy may be associated with optic nerve head drusen.

Omar Solyman,1 Amr Aref,1 Ahmed S Abo Obaia,1 Hesham F Kamel,1 Ahmed Al-Muhaylib2 1Department of Ophthalmology, Research Institute of Ophthalmology, Giza, 12211, Egypt; 2Department of Ophthalmology, School of Medicine, Qassim University, Al-Qassim, Saudi ArabiaCorrespondence: Omar Solyman, Department of Ophthalmology, Research Institute of Ophthalmology, Giza, 12211, Egypt, Email o.solyman@rio.edu.egPurpose: We describe a novel method of digital, virtual-reality based binocular indirect ophthalmoscopy which allows for simultaneous stereoscopic recording of the examination with the potential of real-time anatomic correction of the retinal view.Materials and Methods: A provisional prototype of the all-digital, binocular, indirect virtual stereo video ophthalmoscope was designed consisting of a generic LED light source and two synchronized closely spaced side-by-side minicameras which are connected to a processor, storage media (a Samsung note-9 android smartphone in the current provisional prototype), and a virtual reality set (VISIONHMD Bigeyes H1 3D Video Glasses, in the current prototype). A custom designed android application was developed to capture the examination media and allow optional real-time anatomical correction of the examination view. Binocular stereoscopic indirect ophthalmoscopy was attempted on 15 eyes of 15 patients without and with digital real-time anatomic correction of the examination view.Results: Binocular, video, stereo ophthalmoscopic media could be successfully obtained in all 15 patients. Anatomic correction of the examination view as well as a collateral observer’s view could be achieved in all 15 patients.Conclusion: An all-digital, binocular, stereo, video indirect ophthalmoscopy is a feasible alternative for conventional binocular indirect ophthalmoscopy and provides stereoscopic video documentation identical to what the examiner sees. The examination video can be streamed in the real-time of the examination for educational or telemedicine purposes.Keywords: indirect ophthalmoscopy, fundus examination, retinal imaging, telemedicine, medical education

Abstract Introduction We explored the longitudinal relationship between retinal vascular features and dementia incidence over 10 years. Methods Among 584 participants from the Three‐City‐Alienor (3C‐Alienor) population‐based cohort, quantitative retinal vascular features (caliber, tortuosity, fractal dimension) were measured using semi‐automated software. Dementia was actively diagnosed over the follow‐up period. Results One hundred twenty‐eight participants (21.9%) developed dementia over a median of 7.1 years. In Cox proportional hazards models adjusted for sociodemographic characteristics, apolipoprotein E (APOE) ε4, and vascular factors, increased retinal arteriolar tortuosity was associated with all‐cause dementia (hazard ratio per standard deviation increase, 1.21; 95% confidence interval: 1.02–1.44). Wider retinal calibers and a higher venular tortuosity were associated with mixed/vascular dementia, but not Alzheimer's disease. Fractal dimensions were not associated with dementia. Discussion Changes in the retinal microvasculature were associated with dementia risk. More studies are needed to replicate these findings and determine which features might help identify persons at risk at an early stage. HIGHLIGHTS The retinal microvasculature might reflect the brain microvasculature We explored the association between retinal vascular features and incident dementia 584 participants from the Three‐City‐Alienor cohort were followed‐up over 10 years Increased arteriolar tortuosity and venular calibers were associated with dementia risk Retinal imaging might help identify persons at risk of future dementia

Retinal structural and functional changes in humans can be manifestations of different physiological or pathological conditions. Retinal imaging is the only way to directly inspect blood vessels and their pathological changes throughout the whole body non-invasively. Various quantitative analysis metrics have been used to measure the abnormalities of retinal microvasculature in the context of different retinal, cerebral and systemic disorders. Recently developed optical coherence tomography angiography (OCTA) is a non-invasive imaging tool that allows high-resolution three-dimensional mapping of the retinal microvasculature. The identification of retinal biomarkers from OCTA images could facilitate clinical investigation in various scenarios. We provide a framework for extracting computational retinal microvasculature biomarkers (CRMBs) from OCTA images through a knowledge-driven computerized automatic analytical system. Our method allows for improved identification of the foveal avascular zone (FAZ) and introduces a novel definition of vessel dispersion in the macular region. Furthermore, retinal large vessels and capillaries of the superficial and deep plexus can be differentiated, correlating with retinal pathology. The diagnostic value of OCTA CRMBs was demonstrated by a cross-sectional study with 30 healthy subjects and 43 retinal vein occlusion (RVO) patients, which identified strong correlations between OCTA CRMBs and retinal function in RVO patients. These OCTA CRMBs generated through this “all-in-one” pipeline may provide clinicians with insights about disease severity, treatment response and prognosis, aiding in the management and early detection of various disorders.

Ultrasound A-scan is an important tool for quantitative assessment of ocular lesions. However, its usability is limited by the difficulty of accurately localizing the ultrasound probe to a lesion of interest. In this study, a transparent LiNbO3 single crystal ultrasound transducer was fabricated, and integrated with a widefield fundus camera to guide the ultrasound local position. The electrical impedance, phase spectrum, pulse-echo performance, and optical transmission spectrum of the ultrasound transducer were validated. The novel fundus camera-guided ultrasound probe was tested for in vivo measurement of rat eyes. Anterior and posterior segments of the rat eye could be unambiguously differentiated with the fundus photography-guided ultrasound measurement. A model eye was also used to verify the imaging performance of the prototype device in the human eye. The prototype shows the potential of being used in the clinic to accurately measure the thickness and echogenicity of ocular lesions in vivo.

Abstract Scanning laser ophthalmoscopes (SLOs) are used widely for reflectance, fluorescence or autofluorescence photography and less commonly for retroillumination imaging. SLOs scan a visible light or near-infrared radiation laser beam across the retina, collecting light from each retinal spot as it’s illuminated. An SLO’s clinical applications, image contrast and axial resolution are largely determined by an aperture overlying its photodetector. High contrast, reflectance images are produced using small diameter, centered apertures (confocal apertures) that collect retroreflections and reject side-scattered veiling light returned from the fundus. Retroillumination images are acquired with annular on-axis or laterally-displaced off-axis apertures that capture scattered light and reject the retroreflected light used for reflectance imaging. SLO axial resolution is roughly 300 μm, comparable to macular thickness, so SLOs cannot provide the depth-resolved chorioretinal information obtainable with optical coherence tomography’s (OCT’s) 3 μm axial resolution. Retroillumination highlights and shades the boundaries of chorioretinal tissues and abnormalities, facilitating detection of small drusen, subretinal drusenoid deposits and subthreshold laser lesions. It also facilitates screening for large-area chorioretinal irregularities not readily identified with other en face retinal imaging modalities. Shaded boundaries create the perception of lesion elevation or depression, a characteristic of retroillumination but not reflectance SLO images. These illusions are not reliable representations of three-dimensional chorioretinal anatomy and they differ from objective OCT en face topography. SLO retroillumination has been a useful but not indispensable retinal imaging modality for over 30 years. Continuing investigation is needed to determine its most appropriate clinical roles in multimodal retinal imaging.

Abstract Purpose: To assess the changes in optic nerve head and macular microvascular networks after acute intraocular pressure (IOP) rise in healthy eyes versus the eyes of diabetic patients. Methods: In this prospective, interventional, comparative study, 24 eyes of 24 adults including 12 eyes of healthy nondiabetic subjects and 12 eyes with mild or moderate non-proliferative diabetic retinopathy (NPDR) were enrolled. IOP elevation was induced by a suction cup attached to the conjunctiva. IOP and optical coherence tomography angiographic (OCTA) images of the optic disc and macula were obtained before and immediately after the IOP rise. Results: Baseline and post-suction IOPs were not significantly different between the two groups (all P > 0.05). The mean IOP elevation was 13.93 ± 3.41 mmHg among all eyes and was statistically significant as compared to the baseline in both groups (both P < 0.05). After IOP elevation, healthy eyes demonstrated a reduction in the vessel density in the whole image deep and superficial capillary plexuses and parafoveal deep capillary plexus (DCP) (all P < 0.05). In diabetic retinopathy, foveal vessel density at DCP decreased significantly following IOP rise (P = 0.003). In both groups, inside disc vessel density decreased significantly after IOP rise (both P < 0.05), however, no significant change was observed in peripapillary vessel density (both P > 0.05). Conclusion: Acute rise of IOP may induce different levels of microvascular changes in healthy and diabetic eyes. Optic disc microvasculature originating from the posterior ciliary artery may be more susceptible to IOP elevation than that of retinal microvasculature.

Retinal vein occlusions (RVOs) are the second most common retinal vascular disease after diabetic retinopathy, and are a significant cause of visual impairment, especially in the elderly population. RVOs result in visual loss due to macular ischemia, cystoid macular edema (CME), and complications related to neovascularization. Vascular assessment in RVOs traditionally relies on standard fluorescein angiography (FA) for assessment of macular and retinal ischemia, which aids in prognostication and guides intervention. Standard FA has significant limitations—it is time-consuming, requires invasive dye administration, allows for limited assessment of the peripheral retina, and is usually evaluated semi-qualitatively, by ophthalmologists with tertiary expertise. More recently, the introduction of ultra-widefield FA (UWF FA) and optical coherence tomography angiography (OCTA) into clinical practice has changed the tools available for vascular evaluation in RVOs. UWF FA allows for evaluation of peripheral retinal perfusion, and OCTA is non-invasive, rapidly-acquired, and provides more information on capillary perfusion. Both modalities can be used to provide more quantitative parameters related to retinal perfusion. In this article, we review the clinical utility and impact of UWF FA and OCTA in the evaluation and management of patients with RVOs.

Long-term use of hydroxychloroquine can cause retinopathy, which may result in severe and progressive visual loss. In the past decade, hydroxychloroquine use has markedly increased and modern retinal imaging techniques have enabled the detection of early, pre-symptomatic disease. As a consequence, the prevalence of retinal toxicity in long-term hydroxychloroquine users is known to be higher than was previously estimated. The pathophysiology of the retinopathy is incompletely characterised, although significant advances have been made in understanding the disease from clinical imaging studies. Hydroxychloroquine retinopathy elicits sufficient public health concern to justify the implementation of retinopathy screening programs for patients at risk. Here, we describe the historical background of hydroxychloroquine retinopathy and summarize its current understanding. We review the utility and limitations of each of the mainstream diagnostic tests used to detect hydroxychloroquine retinopathy. The key considerations towards a consensus on the definition of hydroxychloroquine retinopathy are outlined in the context of what is known of the natural history of the disease. We compare the current screening recommendations for hydroxychloroquine retinopathy, identifying where additional evidence is required, and the management of proven cases of toxicity. Finally, we highlight the areas for further investigation, which may further reduce the risk of visual loss in hydroxychloroquine users.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. While the diagnosis of PD primarily relies on clinical assessments and neurological examination, there has been growing interest in exploring non-invasive imaging techniques to aid in early detection and monitoring of the disease. In recent years, retinal imaging has emerged as a promising tool for studying PD due to the close anatomical and functional similarities between the retina and the brain. Retinal imaging methods, such as spectral domain optical coherence tomography and optical coherence tomography angiography, enable non-intrusive visualization and measurement of retinal structures and blood vessels. These techniques hold the promise of capturing alterations in retinal structure and function that could potentially mirror the underlying pathological mechanisms in PD. This review article aims to provide an overview of the current understanding of retinal changes in PD and the potential utility of retinal imaging as a diagnostic and monitoring tool.

Itika Garg,1,2 Raviv Katz,1 Yifan Lu,1,2 John B Miller1,2 1Harvard Retinal Imaging Lab, Boston, MA, USA; 2Retina Service, Massachusetts Eye and Ear Infirmary, Boston, MA, USACorrespondence: John B Miller, Tel +1 617 573-3750, Fax +1 617 573-3698, Email john_miller@meei.harvard.eduPurpose: The COVID-19 pandemic has had a widespread impact on the health sector worldwide, both in the clinical and research subsector. This has necessitated for the invention of protocols compliant with social distancing policies to efficiently tackle the current needs. Our aim is to describe a novel remote imaging technique, which helped us continue our research with widefield swept-source optical coherence tomography angiography (WF SS-OCTA).Patients and Methods: As a part of this cross-sectional observational study from August 2020 to September 2020 at Massachusetts Eye and Ear (MEE), we used our institute approved remote desktop, outside of MEE, to gain full access to the on-site WF SS-OCTA device in the imaging room for the purpose of remote imaging. With only the patient being in the imaging room, effective communication was established via video conferencing on an encrypted tablet computer device for the entire procedure.Results: We imaged four patients with various retinal pathologies, aged 32– 69 years. All images obtained were of high quality and signal strength (median score 9/10 for both) and all patients reported a highly satisfactory, comfortable, and safe experience amidst the COVID-19 pandemic.Conclusion: This novel method is better for remote imaging compared to the previously described techniques as it is time and cost effective, requires limited resources without compromising the image quality or patient satisfaction and adheres to the various guidelines for infection control, most importantly social distancing. As a paradigm shift, this can also be employed in future for setups with limited staff like busy emergency departments or a rural setup with limited access or difficult commute, in the “new-normal” era.Keywords: COVID-19, COVID, teleophthalmology, remote imaging, teleimaging, telemedicine, optical coherence tomography angiography, retinal imaging, optical coherence tomography

Based on our previously developed mono-color video-ophthalmoscope a multi-color video-ophthalmoscope was developed. Using narrow band transmission filters, this instrument allows to measure the pulsatile cardiac cycle induced blood volume changes in the human retina for any wavelength in the sensitivity range of the used CMOS-camera. In this key experiment, video sequences (8 s, 25 fps, 200 frames) of the optic nerve head (ONH) were acquire for seven wavelengths between 475 nm and 677 nm one after the other. After image registration of all frames of each video sequence (to compensate for eye movements) and trend correction (to compensate for slow intensity changes), the amplitude of the cardiac cycle induced light intensity changes (pulsatile absorption amplitude PAA) can be calculated for all seven wavelengths. The results confirmed that the spectral distribution of PAA (λ) follows the distribution of the light absorption of blood. The measured values correspond to the absorption of a thin blood layer of about 0.5 μm thickness.

Sarcoidosis is a multisystem granulomatous inflammation that affects multiple organ systems. The spectrum of extraocular and ocular involvement is wide and may precede systemic involvement. The diagnosis of ocular sarcoidosis relies on a combination of clinical findings, laboratory investigations, and radiographic findings. These include but are not limited to serum angiotensin-converting enzyme (ACE), lysozyme, plain-film radiographs of the chest, computed tomography (CT) scans of the chest, pulmonary function testing, bronchoalveolar lavage, and retinal imaging among others. In this review, we highlight current and evolving systemic investigations and approaches to ophthalmic imaging when considering the diagnosis of ocular sarcoidosis.