Ultra-widefield Retinal Imaging Research Articles

Prevalence and spectrum of manifestations of tamoxifen retinopathy as assessed by multimodal and ultra-widefield retinal imaging

Background/ObjectivesTamoxifen has well-known retinal toxicity, but the epidemiology of this toxicity is poorly defined. This study aims to 1) evaluate the prevalence of tamoxifen retinopathy in a major northeastern metropolitan area based on multimodal retinal imaging and 2) determine whether the additional peripheral retina captured in ultra-widefield (UWF) imaging aids in the diagnosis of tamoxifen retinopathy. MethodsThis retrospective cohort study examined female patients initiated on tamoxifen for at least 12 months prior to their first retinal exam. Two independent graders performed blinded review of spectral-domain optical coherence tomography (OCT) images for evidence of macular toxicity and UWF images for signs of central and peripheral toxicity. A one-tailed two-proportion Z-test determined whether peripheral pigmentary changes were more prevalent in the tamoxifen cohort than age- and gender-matched controls. Results241 eyes from 123 patients were included in the tamoxifen cohort, and 258 eyes from 132 patients were included in the control cohort. Two patients demonstrated definitive tamoxifen retinopathy, for a prevalence of 1.6%. One patient demonstrated crystalline maculopathy on OCT and UWF imaging, while another demonstrated pseudocystic cavitations on OCT imaging. Neither patient had peripheral findings on UWF imaging. Peripheral pigmentary changes were not significantly more prevalent in the tamoxifen cohort. ConclusionsThese findings suggest that UWF imaging does not contribute to diagnosis of tamoxifen retinopathy, and OCT may be the most valuable tool in diagnosing tamoxifen retinopathy. Our prevalence (1.6%) diverges from higher rates in studies screening with similarly advanced retinal imaging. Further large population studies are needed.

Automatic Segmentation of Hemorrhages in the Ultra-wide Field Retina: Multi-scale Attention Subtraction Networks and An Ultra-wide Field Retinal Hemorrhage Dataset.

Ultra-wide field (UWF) retinal imaging can improve the detection rate of retinal hemorrhage as compared with conventional fundus images. However, hemorrhages in UWF retinal images can also become smaller and more widely distributed, which can be time consuming and labor intensive. With the development of computer technology, automatic segmentation techniques can assist physicians in diagnosis. However, the lack of publicly available UWF retinal hemorrhage segmentation datasets has limited the development of automatic hemorrhage segmentation techniques in UWF retinal images. We present a large-scale high-quality UWF retinal hemorrhage segmentation dataset, named UWF-RHS Dataset, for public use. To the best of our knowledge, we are the first team to make the UWF retinal hemorrhage segmentation dataset publicly available. In addition, we propose a multi-scale attention subtraction network (MASNet) for UWF retinal hemorrhage segmentation. Specifically, highly focused lesion features are extracted by using the proposed multi-scale attention subtraction (MAS) module at the progress of the skip-connection. Several comparative experiments and ablation experiments were performed at the UWF-RHS Dataset, and all experiments state that our proposed method is effective in diagnosing retinal hemorrhages with state-of-the-art results. The proposed UWF-RHS dataset and MASNet will greatly facilitate the development of automated segmentation techniques for UWF retinal hemorrhages. Links to the UWF-RHS dataset and the MASNet model code are available fromhttps://github.com/wurenkai/UWF-RHS-Dataset-and-MASNet.

FQ-UWF: Unpaired Generative Image Enhancement for Fundus Quality Ultra-Widefield Retinal Images.

Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular degeneration. Conventional supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truths. However, obtaining such refined paired ground truths is a formidable challenge. To tackle this issue, we propose an unpaired, degradation-aware, super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning: specifically, by employing generative adversarial networks and attention mechanisms. Notably, our method excels at enhancing and super-resolving UWF images without relying on paired, clean ground truths. Through extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal images. We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes.

Colour fusion effect on deep learning classification of uveal melanoma.

Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of colour fusion options on the classification performance. A total of 798 ultra-widefield retinal images of 438 patients were included in this retrospective study, comprising 157 patients diagnosed with UM and 281 patients diagnosed with choroidal naevus. Colour fusion options, including early fusion, intermediate fusion and late fusion, were tested for deep learning image classification with a convolutional neural network (CNN). F1-score, accuracy and the area under the curve (AUC) of a receiver operating characteristic (ROC) were used to evaluate the classification performance. Colour fusion options were observed to affect the deep learning performance significantly. For single-colour learning, the red colour image was observed to have superior performance compared to green and blue channels. For multi-colour learning, the intermediate fusion is better than early and late fusion options. Deep learning is a promising approach for automated classification of uveal melanoma and choroidal nevi. Colour fusion options can significantly affect the classification performance.

Progress and challenges of in vivo flow cytometry and its applications in circulating cells of eyes.

Circulating inflammatory cells in eyes have emerged as early indicators of numerous major diseases, yet the monitoring of these cells remains an underdeveloped field. In vivo flow cytometry (IVFC), a noninvasive technique, offers the promise of real-time, dynamic quantification of circulating cells. However, IVFC has not seen extensive applications in the detection of circulating cells in eyes, possibly due to the eye's unique physiological structure and fundus imaging limitations. This study reviews the current research progress in retinal flow cytometry and other fundus examination techniques, such as adaptive optics, ultra-widefield retinal imaging, multispectral imaging, and optical coherence tomography, to propose novel ideas for circulating cell monitoring.

Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images

BackgroundArtificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on...

Automated Machine Learning for Predicting Diabetic Retinopathy Progression From Ultra-Widefield Retinal Images

Machine learning (ML) algorithms have the potential to identify eyes with early diabetic retinopathy (DR) at increased risk for disease progression. To create and validate automated ML models (autoML) for DR progression from ultra-widefield (UWF) retinal images. Deidentified UWF images with mild or moderate nonproliferative DR (NPDR) with 3 years of longitudinal follow-up retinal imaging or evidence of progression within 3 years were used to develop automated ML models for predicting DR progression in UWF images. All images were collected from a tertiary diabetes-specific medical center retinal image dataset. Data were collected from July to September 2022. Automated ML models were generated from baseline on-axis 200° UWF retinal images. Baseline retinal images were labeled for progression based on centralized reading center evaluation of baseline and follow-up images according to the clinical Early Treatment Diabetic Retinopathy Study severity scale. Images for model development were split 8-1-1 for training, optimization, and testing to detect 1 or more steps of DR progression. Validation was performed using a 328-image set from the same patient population not used in model development. Area under the precision-recall curve (AUPRC), sensitivity, specificity, and accuracy. A total of 1179 deidentified UWF images with mild (380 [32.2%]) or moderate (799 [67.8%]) NPDR were included. DR progression was present in half of the training set (590 of 1179 [50.0%]). The model's AUPRC was 0.717 for baseline mild NPDR and 0.863 for moderate NPDR. On the validation set for eyes with mild NPDR, sensitivity was 0.72 (95% CI, 0.57-0.83), specificity was 0.63 (95% CI, 0.57-0.69), prevalence was 0.15 (95% CI, 0.12-0.20), and accuracy was 64.3%; for eyes with moderate NPDR, sensitivity was 0.80 (95% CI, 0.70-0.87), specificity was 0.72 (95% CI, 0.66-0.76), prevalence was 0.22 (95% CI, 0.19-0.27), and accuracy was 73.8%. In the validation set, 6 of 9 eyes (75%) with mild NPDR and 35 of 41 eyes (85%) with moderate NPDR progressed 2 steps or more were identified. All 4 eyes with mild NPDR that progressed within 6 months and 1 year were identified, and 8 of 9 (89%) and 17 of 20 (85%) with moderate NPDR that progressed within 6 months and 1 year, respectively, were identified. This study demonstrates the accuracy and feasibility of automated ML models for identifying DR progression developed using UWF images, especially for prediction of 2-step or greater DR progression within 1 year. Potentially, the use of ML algorithms may refine the risk of disease progression and identify those at highest short-term risk, thus reducing costs and improving vision-related outcomes.

Prevalence of peripheral retinal findings in retinal patients using ultra-widefield pseudocolor fundus imaging

Ultra-widefield retinal imaging is increasingly used in ophthalmology and optometry practices to image patients identifying peripheral abnormalities. However, the clinical relevance of these peripheral retinal abnormalities is unclear. This cross-sectional study aims to firstly validate a new grading system, secondly, assess the prevalence of peripheral retinal abnormalities in retinal patients, and finally understand how peripheral findings may associate with retinal disease. Ultra-widefield pseudocolor fundus images were taken from the eyes of clinic patients. Demographic data and clinical diagnosis for each patient was noted. The grading system was validated using masked retinal specialists. Logistic regression identified associations between retinal disease and peripheral retinal findings. Using the grading system, inter-observer agreement was 76.1% with Cohen’s Kappa coefficient 0.542 (p < 0.0001) and the test–retest agreement was 95.1% with Kappa 0.677(p < 0.0001). 971 images were included, with 625 eyes (64.4%) having peripheral abnormalities. Peripheral drusen was the most common abnormality (n = 221, 22.76%) and correlated with age-related macular degeneration (p < 0.001). Novel correlations were also identified between diabetic retinopathy and retinal pigmentation as well as pigmentary degeneration. This study provides a validated system for identifying peripheral abnormalities and adds to literature highlighting peripheral retinal associations with retinal disease which would benefit from further study.

INTEGRATING MACULAR OPTICAL COHERENCE TOMOGRAPHY WITH ULTRAWIDE-FIELD IMAGING IN A DIABETIC RETINOPATHY TELEMEDICINE PROGRAM USING A SINGLE DEVICE.

To determine the effect of combined macular spectral-domain optical coherence tomography (SD-OCT) and ultrawide field retinal imaging (UWFI) within a telemedicine program. Comparative cohort study of consecutive patients with both UWFI and SD-OCT. Ultrawide field retinal imaging and SD-OOCT were independently evaluated for diabetic macular edema (DME) and nondiabetic macular abnormality. Sensitivity and specificity were calculated with SD-OCT as the gold standard. Four hundred twenty-two eyes from 211 diabetic patients were evaluated. Diabetic macular edema severity by UWFI was as follows: no DME 93.4%, noncenter involved DME (nonciDME) 5.1%, ciDME 0.7%, ungradable DME 0.7%. SD-OCT was ungradable in 0.5%. Macular abnormality was identified in 34 (8.1%) eyes by UWFI and in 44 (10.4%) eyes by SD-OCT. Diabetic macular edema represented only 38.6% of referable macular abnormality identified by SD-OCT imaging. Sensitivity/specificity of UWFI compared with SD-OCT was 59%/96% for DME and 33%/99% for ciDME. Sensitivity/specificity of UWFI compared with SDOCT was 3%/98% for epiretinal membrane. Addition of SD-OCT increased the identification of macular abnormality by 29.4%. More than 58.3% of the eyes believed to have any DME on UWF imaging alone were false-positives by SD-OCT. The integration of SD-OCT with UWFI markedly increased detection and reduced false-positive assessments of DME and macular abnormality in a teleophthalmology program.

26-LB: Identifying the Risk of Diabetic Retinopathy Progression Using Machine Learning on Ultrawide Field Retinal Images

Estimating the risk of diabetic retinopathy (DR) progression is one of the most important and difficult tasks when caring for individuals with diabetic eye disease. Current DR severity scales have informed clinicians on the progression risk and provided recommendations for follow-up and treatment. The use of artificial intelligence (AI) algorithms may improve in this process. In this study, we developed and validated machine learning (ML) models for DR progression from ultrawide field (UWF) retinal images, which were labeled for baseline DR severity and progression based on clinician review of the images and 3-year longitudinal follow-up using the Early Treatment Diabetic Retinopathy Study (ETDRS) severity scale. This dataset has 8 classes: no DR nonprogression (14.62%), Mild nonproliferative DR (NPDR) progression (10.16%) /nonprogression (10.73%), Moderate NPDR progression (10.1%) /nonprogression (15.85%), Severe NPDR progression (11.27%) /nonprogression (10.68%), and proliferative DR (16.55%). A total of 9970 unique images were split into the train, the validation, and the test datasets based on 60-20-20 proportions. The class imbalance was addressed during model building through data augmentation. The ResNet model fine-tuned on this dataset has a classification test accuracy of 81% and an AUC of 0.967 on the test dataset. The objective of the model is to reduce false negatives, which refers to predicting a class that is less progressive than the true label. The predicted labels for 91% of the images were either correct labels or were the labels with greater progression than the original labels. These findings demonstrate the accuracy and feasibility of using machine learning models for identifying DR progression developed using UWF images. Potentially, the use of machine learning algorithms may further refine the risk of disease progression and personalize screening intervals that may reduce costs and improve vision-related outcomes. Disclosure A. Nigam: None. J. Sun: None. V. Subhash: None. P. S. Silva: Research Support; Optos plc., Optomed, Speaker's Bureau; Novartis, Roche Pharmaceuticals, Bayer Inc.

Circumscribed Choroidal Hemangioma Simulating Choroidal Melanoma on Advanced Ultrawide-Field Pseudocolor Retinal Imaging: A Case Series

The purpose of this case series was to evaluate the features of circumscribed choroidal hemangioma on pseudocolor ultrawide-field (UWF) retinal images simulating choroidal melanoma and compare it to fundoscopic appearance. All four patients underwent full ophthalmological examination, including dilated fundus examination, ultrasonography, and UWF imaging (UWFI). All circumscribed choroidal hemangioma appeared clinically as orange-red choroidal lesions which were echodense with regular internal structure on ultrasonography. All lesions appeared green-grey in color on pseudocolor UWFI. Pseudocolor UWFI of circumscribed choroidal hemangioma represents distortion of true color appearance and may simulate choroidal melanoma. [Ophthalmic Surg Lasers Imaging Retina 2023;54:292-296.].

The big warp: Registration of disparate retinal imaging modalities and an example overlay of ultrawide-field photos and en-face OCTA images.

To develop an algorithm and scripts to combine disparate multimodal imaging modalities and show its use by overlaying en-face optical coherence tomography angiography (OCTA) images and Optos ultra-widefield (UWF) retinal images using the Fiji (ImageJ) plugin BigWarp. Optos UWF images and Heidelberg en-face OCTA images were collected from various patients as part of their routine care. En-face OCTA images were generated and ten (10) images at varying retinal depths were exported. The Fiji plugin BigWarp was used to transform the Optos UWF image onto the en-face OCTA image using matching reference points in the retinal vasculature surrounding the macula. The images were then overlayed and stacked to create a series of ten combined Optos UWF and en-face OCTA images of increasing retinal depths. The first algorithm was modified to include two scripts that automatically aligned all the en-face OCTA images. The Optos UWF image could easily be transformed to the en-face OCTA images using BigWarp with common vessel branch point landmarks in the vasculature. The resulting warped Optos image was then successfully superimposed onto the ten Optos UWF images. The scripts more easily allowed for automatic overlay of the images. Optos UWF images can be successfully superimposed onto en-face OCTA images using freely available software that has been applied to ocular use. This synthesis of multimodal imaging may increase their potential diagnostic value. Script A is publicly available at https://doi.org/10.6084/m9.figshare.16879591.v1 and Script B is available at https://doi.org/10.6084/m9.figshare.17330048.

Risk Factors for Epiretinal Membrane After Rhegmatogenous Retinal Detachment Repair: Quantitative Ultra Widefield Imaging Analysis

To utilize quantitative ultra widefield retinal imaging to investigate the risk factors associated with the incidence and severity of postoperative epiretinal membrane (ERM) in patients who underwent primary rhegmatogenous retinal detachment (RRD) repair. This retrospective study included patients who underwent pars plana vitrectomy (PPV) with scleral buckle (SB) for RRD, without a prior history of ERM, and who underwent ultra widefield imaging postoperatively between June 2020 and February 2022. The size and location (distance from the macula) of the drainage retinotomy and the area of peripheral pathology, including retinal breaks and laser scars, were quantitatively measured with cloud-based software. The severity of postoperative ERM formation at 6 months was graded from grade 1 to 4. We investigated the risk factors that were significantly associated with the incidence and severity of postoperative ERM at 6 months. We included 80 eyes (80 patients) in this study. Postoperative ERM developed in 37 eyes (46%) at 6 months. The severity of ERM was grade 1 in 24 eyes (65%), grade 2 in 6 eyes (16%), grade 3 in 4 eyes (11%), and grade 4 in 3 eyes (8%). Postoperative ERM was not associated with the presence or the location of drainage retinotomy (P = 0.836 and 0.820, respectively). However, it was significantly associated with larger surface area of drainage retinotomy (P = 0.039). In addition, postoperative ERM was significantly associated with a larger area of peripheral pathology (P = 0.012), a larger extent of RRD (P = 0.013), vitreous hemorrhage (P = 0.026), redetachment within 6 months (P = 0.022), use of silicone oil as a tamponade (P = 0.047), and number of surgeries within 6 months (P = 0.027). These factors, in addition to 360° endolaser, were also significantly associated with the severity of postoperative ERM. In multivariable linear regression analysis, the only variable that remained statistically significant was the size of the drainage retinotomy (P = 0.023). The pathogenesis of postoperative ERM is multifactorial. Large drainage retinotomies may increase the risk of ERM formation. [Ophthalmic Surg Lasers Imaging Retina 2023;54:206-216.].

MODERN DIAGNOSTIC POSSIBILITIES FOR THE ASSESSMENT OF POSTERIOR EYE DISEASES WITH ULTRA-WIDEFIELD IMAGING

Abstract: Ultra-widefield retinal imaging is a modern method of imaging the posterior segment of the eye that enables scanning of the entire far periphery of retina. Our study presents the diagnostic possibilities of this technique using Optos California to illustrate pathology anywhere in the retina and provide clinical data from the retinal surface through the choroid.

Abstract P1-12-06: Prevalence and Spectrum of Manifestations of Tamoxifen Retinopathy as Assessed by Multimodal and Ultra-widefield Retinal Imaging

Abstract Introduction Tamoxifen, a selective estrogen antagonist, is part of standard adjuvant therapy for hormone receptor-positive breast cancer. In addition to known systemic toxicities, well-described retinotoxic effects include peripheral and macular crystalline deposits, pseudocystic foveal cavitations, as well as an increased risk of developing punctate pigmentary changes. However, the true prevalence of retinal toxicity is unclear, with the literature reporting rates between 1.5% and 11%, with significant geographic and temporal discrepancies depending on choice of screening modality. Advances in retinal imaging, specifically spectral domain and swept-source optical coherence tomography (OCT), have led to more sensitive diagnosis of toxicity. However, the field of view of commercially available OCT is limited 30 degrees, whereas ultra-widefield (UWF) imaging can image up to 200 degrees of the retina, albeit with a superficial rather than a cross-sectional view. Therefore, UWF imaging may visualize previously underreported signs of toxicity in the peripheral retina such as pigmentary changes better than macula-focused imaging alone like OCT. As such, the goals of the present study were to: 1) determine the prevalence of tamoxifen retinopathy in our cohort, a major northeastern metropolitan area, based on multimodal retinal imaging including macular OCT, fundus photos, and autofluorescence; 2) determine whether the additional field of view captured in UWF imaging contributes to the diagnosis of tamoxifen retinopathy by assessing for peripheral findings, most specifically pigmentary retinopathy. Methods This is a retrospective study of patients who visited the retina service of a single academic tertiary referral center between September 2012 and November 2021. Female patients initiated on tamoxifen for at least 6 months before their first retinal exam were identified. Patients with poor image quality, prior vitreoretinal surgery, retinal laser, or confounding pathology such as severe diabetic retinopathy were excluded. Two independent graders performed blinded review of OCT images for evidence of macular toxicity and UWF images for signs of central and peripheral toxicity, and the prevalence of the latter was compared to age- and gender-matched controls. A two-tailed t-test was used to compare ages of patients in the two cohorts. A one-tailed two proportion Z-test was used to determine if eyes in the tamoxifen cohort exhibited a greater rate of peripheral pigmentary changes. Results 252 eyes from 128 patients were included in the tamoxifen cohort, and 244 eyes from 125 patients in the control cohort. The average age at first retinal imaging in the tamoxifen cohort (61.1 ± 1.1, n=128) versus the control cohort (61.0 ± 1.3, n = 125) was not significantly different (p=0.95). 4 eyes (1.6%) from 2 patients (1.6%) had evidence of definitive tamoxifen retinopathy. One patient had crystalline maculopathy bilaterally visible on OCT and UWF imaging and another had macular pseudocystic cavitations bilaterally on OCT. Neither patient had peripheral findings on UWF. 31 eyes (12.4%) among 16 (12.5%) patients in the tamoxifen cohort displayed peripheral reticular pigmentary changes, compared to 59 eyes (24.2%) among 30 patients (24.0%) in the control group. The rate of peripheral pigmentary change in the tamoxifen cohort was not significantly greater than in the control group (p=0.99). Conclusion Only 2 patients had definitive tamoxifen retinopathy findings, which were both concentrated in the macula and clearly visible on OCT imaging. Given the absence of meaningful UWF changes, macular OCT may be the most valuable tool in diagnosing tamoxifen retinopathy. Our prevalence (1.6%) diverges from higher prevalences reported by studies screening with advanced retinal imaging (10-12%), though notably reflects a very different ethnic and geographic population compared with other work. Further large population studies are needed. Citation Format: Ethan Zhao, Kyle Kovacs. Prevalence and Spectrum of Manifestations of Tamoxifen Retinopathy as Assessed by Multimodal and Ultra-widefield Retinal Imaging [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-12-06.

Combining Feature Correspondence With Parametric Chamfer Alignment: Hybrid Two-Stage Registration for Ultra-Widefield Retinal Images.

We propose a novel hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in ultra-widefield (UWF) fluorescein angiography. Our approach consists of two stages: a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC (random sample and consensus) that jointly identifies robust matches between feature keypoints in reference and target images and estimates a polynomial geometric transformation consistent with the identified correspondences. Our RANSAC modification particularly improves feature point matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The second local refinement stage is formulated in our framework as a parametric chamfer alignment for vessel maps obtained using a deep neural network. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. We validate the effectiveness of the proposed framework on a new UWF fluorescein angiography (FA) dataset and on the existing narrow-field FIRE (fundus image registration) dataset and demonstrate that it significantly outperforms prior retinal image registration methods in accuracy. The proposed approach enhances the utility of large sets of longitudinal UWF images by enabling: (a) automatic computation of vessel change metrics such as vessel density and caliber, and (b) standardized and co-registered examination that can better highlight changes of clinical interest to physicians.

Retinal Vascular Changes in Alzheimer's Dementia and Mild Cognitive Impairment: A Pilot Study Using Ultra-Widefield Imaging.

Retinal microvascular abnormalities measured on retinal images are a potential source of prognostic biomarkers of vascular changes in the neurodegenerating brain. We assessed the presence of these abnormalities in Alzheimer's dementia and mild cognitive impairment (MCI) using ultra-widefield (UWF) retinal imaging. UWF images from 103 participants (28 with Alzheimer's dementia, 30 with MCI, and 45 with normal cognition) underwent analysis to quantify measures of retinal vascular branching complexity, width, and tortuosity. Participants with Alzheimer's dementia displayed increased vessel branching in the midperipheral retina and increased arteriolar thinning. Participants with MCI displayed increased rates of arteriolar and venular thinning and a trend for decreased vessel branching. Statistically significant differences in the retinal vasculature in peripheral regions of the retina were observed among the distinct cognitive stages. However, larger studies are required to establish the clinical importance of our findings. UWF imaging may be a promising modality to assess a larger view of the retinal vasculature to uncover retinal changes in Alzheimer's disease. This pilot work reports an investigation into which retinal vasculature measurements may be useful surrogate measures of cognitive decline, as well as technical developments (e.g., measurement standardization), that are first required to establish their recommended use and translational potential.

A Prospective Audit Comparing Optos Widefield Imaging to Fundus Examination for Von Hippel-Lindau Retinal Screening.

Background Von Hippel-Lindau (VHL) disease is an autosomal dominant multisystem disorder caused by germline mutations at chromosome 3p25-26 in the VHLtumour suppressor gene. Retinal manifestations include capillary haemangiomas that develop in up to 80% of gene carriers. Lifelong retinal surveillance involves yearly assessment usually by fundoscopy and often as part of a VHL multidisciplinary clinic. Optos ultra-widefield retinal imaging is now becoming more widely used in virtual retinal screening clinics. We aimed to assess discrepancies in the pickup rate of angioma and angiomatous-associated disease between slit-lamp fundoscopy and Optos ultra-widefield imaging. Methodology A total of 49 patients had both Optos ultra-widefield retinal imaging and slit-lamp fundoscopy over16 months in VHL retinal surveillance clinics at the John Radcliffe Hospital, Oxford, UK. Optos images were analysed for image quality and presence of angioma(s) by a Consultant Ophthalmologist who was masked to the fundoscopy findings. The pickup rate was compared between slit-lamp fundoscopy and Optos imaging. Results In total, data on 94 eyes were collected. Of the total Optos retinal images,12.8% were positive for angiomas compared to 11.7% from the slit-lamp examination. There was a discrepancy of 1.1% (one value) where the Optos image analysis suggested a possible angioma, which was not identified on slit-lamp examination. Optos imaging identified all angiomas in this cohort. Conclusions Optos imaging was non-inferior to slit-lamp examination in this sample of 94 eyes. In the current COVID-19 climate, reducing clinician-patient interaction is important. This research supports providing retinal imaging as an acceptable alternative to the yearly slit-lamp fundus examination.

Classification of diabetic retinopathy: Past, present and future.

Diabetic retinopathy (DR) is a leading cause of visual impairment and blindness worldwide. Since DR was first recognized as an important complication of diabetes, there have been many attempts to accurately classify the severity and stages of disease. These historical classification systems evolved as understanding of disease pathophysiology improved, methods of imaging and assessing DR changed, and effective treatments were developed. Current DR classification systems are effective, and have been the basis of major research trials and clinical management guidelines for decades. However, with further new developments such as recognition of diabetic retinal neurodegeneration, new imaging platforms such as optical coherence tomography and ultra wide-field retinal imaging, artificial intelligence and new treatments, our current classification systems have significant limitations that need to be addressed. In this paper, we provide a historical review of different classification systems for DR, and discuss the limitations of our current classification systems in the context of new developments. We also review the implications of new developments in the field, to see how they might feature in a future, updated classification.

Comparison of Widefield Laser Ophthalmoscopy and ETDRS Retinal Area for Diabetic Retinopathy

To evaluate agreement of nonmydriatic confocal scanning laser ophthalmoscopy (SLO; EIDON [CenterVue]) and the 7-standard field ETDRS area on ultrawide-field (UWF) SLO imaging for identification of diabetic retinopathy (DR) severity. Single-site, prospective, comparative, instrument validation study. One hundred ten eyes of 55 patients with diabetes mellitus were evaluated. Each patient underwent nonmydriatic, nonsimultaneous stereoscopic imaging using the EIDON camera and 4 fields of 60°× 55° were acquired (macula centered, disc centered, temporal macula, superotemporal). Mydriatic UWF retinal images were acquired using a nonsimultaneous stereographic protocol with UWF imaging (California; Optos plc). Before grading, a standardized ETDRS 7-field image mask was applied to all UWF retinal images. Images from each device were graded independently by 2 masked graders using the ETDRS clinical DR classification. Any discrepancy in DR grading between the devices was adjudicated by a third grader. κ Levels of agreement, sensitivity, and specificity for DR thresholds. Severity by ETDRS grading was as follows: no DR, 10.9%; mild nonproliferative DR (NPDR), 45.5%; moderate NPDR, 16.5%; severe NPDR, 11.8%; proliferative DR, 12.7%; high-risk proliferative DR, 2.7%; and ungradable, 0%. After adjudication, the level of DR identified on EIDON images agreed exactly with that of UWF ETDRS imaging in 87% of eyes (n= 96) and was within 1 step in 99.1% of eyes (n= 109) with a simple κ value of 0.8244 ± 0.0439 (95% confidence interval [CI], 0.7385-0.9104) and weighted (linear) κ value of 0.9041 ± 0.0257 (95% CI, 0.8537-0.9545). Sensitivity and specificity compared with ETDRS field grading for any DR were 0.96 and 0.75, for moderate NPDR or worse were 0.96 and 0.97, and for severe NPDR or worse were 0.91 and 1.00, respectively. Nonmydriatic 4-field stereoscopic widefield imaging using the EIDON device was comparable with the DR severity identified within the ETDRS 7-standard field area of UWF images. Future studies will need to evaluate the applicability of this device as a clinical and research tool and the impact of different widefield coverage areas.