Convolutional neural network-based classification of glaucoma using optic radiation tissue properties

BACKGROUND

Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections.

METHODS

We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models.

RESULTS

We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration.

CONCLUSIONS

Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.

Sight threatening diabetic retinopathy in patients with macular telangiectasia type 2

PURPOSE

Although diabetes is highly prevalent in patients with MacTel, progression to severe non-proliferative (NPDR) and proliferative diabetic retinopathy (PDR) is rarely reported. We report multimodal imaging features of sight-threatening diabetic retinopathy (STDR) in eyes with macular telangiectasia type 2 (MacTel).

METHODS

Retrospective case series of seven participants of the MacTel Study at the Moorfields Eye Hospital NHS Foundation Trust study site and one patient from the Institute of Retina and Vitreous of Londrina, Brazil. Sight threatening diabetic retinopathy was defined as severe NPDR, PDR or diabetic macular edema.

RESULTS

We report imaging features of 16 eyes of eight patients (7/8, 87.5% female) with diagnoses of MacTel and type 2 diabetes mellitus with STDR. Mean (SD) age was 56 (8.3) years. Patients were followed-up for a mean time of 9.1 (4.7) years. A total of 10/16 (62.5%) eyes showed PDR and 2/16 (12.5%) eyes presented a macular epiretinal neovascularization.

CONCLUSIONS

People with diabetes mellitus and MacTel may not be protected from STDR as previously reported. Although the two diseases rarely co-exist, regular monitoring for diabetic retinopathy progression is recommended according to baseline retinopathy severity grades in line with established international guidelines. The presence of MacTel may not modify extended screening intervals, but there is no current evidence. The limited case series in the literature support treatment for complications and should follow the standard of care for either condition. Due to dual pathology, reactivation may be difficult to diagnose on standard imaging and multimodal imaging is recommended.

Developing a Continuous Severity Scale for Macular Telangiectasia Type 2 Using Deep Learning and Implications for Disease Grading

PURPOSE

Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP).

DESIGN

We used a DL network to learn a feature representation of MacTel severity from discrete severity labels and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale.

PARTICIPANTS

A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants.

METHODS

We trained a multiview DL classifier using multiple B-scans from OCT volumes to learn a previously published discrete 7-step MacTel severity scale. The classifiers' last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2-dimensional manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against the previously published scale was calculated. Additionally, the UMAP scale was assessed in the κ agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease and to compare them against the UMAP scale.

MAIN OUTCOME MEASURES

Classification accuracy for the DL classifier and κ agreement versus clinical experts for UMAP.

RESULTS

The multiview DL classifier achieved top 1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric showed a clear continuous gradation of MacTel severity with a Spearman rank correlation of 0.84 with the previously published scale. Furthermore, the continuous UMAP metric achieved κ agreements of 0.56 to 0.63 with 5 clinical experts, which was comparable with interobserver κ values.

CONCLUSIONS

Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases and may lead to more accurate diagnosis, improved understanding of disease progression, and key imaging features for pathologic characteristics.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Human selection bias drives the linear nature of the more ground truth effect in explainable deep learning optical coherence tomography image segmentation

Supervised deep learning (DL) algorithms are highly dependent on training data for which human graders are assigned, for example, for optical coherence tomography (OCT) image annotation. Despite the tremendous success of DL, due to human judgment, these ground truth labels can be inaccurate and/or ambiguous and cause a human selection bias. We therefore investigated the impact of the size of the ground truth and variable numbers of graders on the predictive performance of the same DL architecture and repeated each experiment three times. The largest training dataset delivered a prediction performance close to that of human experts. All DL systems utilized were highly consistent. Nevertheless, the DL under-performers could not achieve any further autonomous improvement even after repeated training. Furthermore, a quantifiable linear relationship between ground truth ambiguity and the beneficial effect of having a larger amount of ground truth data was detected and marked as the more-ground-truth effect.

Retinal vessel volume reference database derived from volume-rendered optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) enables three-dimensional reconstruction of the functional blood vessels in the retina. Therefore, it enables the quantification of 3D retinal vessel parameters such as surface area and vessel volume. In spite of the widespread use of OCTA, no representative volume-rendered vessel volume (VV) data are published to date. In this study, OCTA 3 × 3 mm macular cubes were processed with volume-rendering techniques to measure VV in 203 eyes from 107 healthy volunteers. Generalized linear models (GLM) were constructed to assess the impact of age, gender, visual acuity (VA), spherical equivalent (SE), and axial length (AL) on VV. Overall mean VV was 0.23 ± 0.05mm3. Age and axial length showed a negative correlation with VV. However, GLM model analysis found that AL exerted the most pronounced influence on VV. No statistically significant associations were identified between gender or between left and right eyes. This is the first study to assess 3D OCTA VV and its naturally occurring variations in a large series of healthy subjects. It offers novel insights into the characterization of normal retinal vascular anatomy in healthy individuals, contributing to a valuable reference for future research in this field.

Artificial intelligence-supported diabetic retinopathy screening in Tanzania: rationale and design of a randomised controlled trial

INTRODUCTION

Globally, diabetic retinopathy (DR) is a major cause of blindness. Sub-Saharan Africa is projected to see the largest proportionate increase in the number of people living with diabetes over the next two decades. Screening for DR is recommended to prevent sight loss; however, in many low and middle-income countries, because of a lack of specialist eye care staff, current screening services for DR are not optimal. The use of artificial intelligence (AI) for DR screening, which automates the grading of retinal photographs and provides a point-of-screening result, offers an innovative potential solution to improve DR screening in Tanzania.

METHODS AND ANALYSIS

We will test the hypothesis that AI-supported DR screening increases the proportion of persons with true referable DR who attend the central ophthalmology clinic following referral after screening in a single-masked, parallel group, individually randomised controlled trial. Participants (2364) will be randomised (1:1 ratio) to either AI-supported or the standard of care DR screening pathway. Participants allocated to the AI-supported screening pathway will receive their result followed by point-of-screening counselling immediately after retinal image capture. Participants in the standard of care arm will receive their result and counselling by phone once the retinal images have been graded in the usual way (typically after 2-4 weeks). The primary outcome is the proportion of persons with true referable DR attending the central ophthalmology clinic within 8 weeks of screening. Secondary outcomes, by trial arm, include the proportion of persons attending the central ophthalmology clinic out of all those referred, sensitivity and specificity, number of false positive referrals, acceptability and fidelity of AI-supported screening.

ETHICS AND DISSEMINATION

The London School of Hygiene & Tropical Medicine, Kilimanjaro Christian Medical Centre and Tanzanian National Institute of Medical Research ethics committees have approved the trial. The results will be submitted to peer-reviewed journals for publication.

TRIAL REGISTRATION NUMBER

ISRCTN18317152.

What are the perceptions and concerns of people living with diabetes and National Health Service staff around the potential implementation of AI-assisted screening for diabetic eye disease? Development and validation of a survey for use in a secondary care screening setting

INTRODUCTION

The English National Health Service (NHS) Diabetic Eye Screening Programme (DESP) performs around 2.3 million eye screening appointments annually, generating approximately 13 million retinal images that are graded by humans for the presence or severity of diabetic retinopathy. Previous research has shown that automated retinal image analysis systems, including artificial intelligence (AI), can identify images with no disease from those with diabetic retinopathy as safely and effectively as human graders, and could significantly reduce the workload for human graders. Some algorithms can also determine the level of severity of the retinopathy with similar performance to humans. There is a need to examine perceptions and concerns surrounding AI-assisted eye-screening among people living with diabetes and NHS staff, if AI was to be introduced into the DESP, to identify factors that may influence acceptance of this technology.

METHODS AND ANALYSIS

People living with diabetes and staff from the North East London (NEL) NHS DESP were invited to participate in two respective focus groups to codesign two online surveys exploring their perceptions and concerns around the potential introduction of AI-assisted screening.Focus group participants were representative of the local population in terms of ages and ethnicity. Participants' feedback was taken into consideration to update surveys which were circulated for further feedback. Surveys will be piloted at the NEL DESP and followed by semistructured interviews to assess accessibility, usability and to validate the surveys.Validated surveys will be distributed by other NHS DESP sites, and also via patient groups on social media, relevant charities and the British Association of Retinal Screeners. Post-survey evaluative interviews will be undertaken among those who consent to participate in further research.

ETHICS AND DISSEMINATION

Ethical approval has been obtained by the NHS Research Ethics Committee (IRAS ID: 316631). Survey results will be shared and discussed with focus groups to facilitate preparation of findings for publication and to inform codesign of outreach activities to address concerns and perceptions identified.

Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study

INTRODUCTION

The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual eye screening. We examined incidence and determinants of sight-threatening diabetic retinopathy (STDR) in a sociodemographically diverse multi-ethnic population.

RESEARCH DESIGN AND METHODS

North East London DESP cohort data (January 2012 to December 2021) with 137 591 PLD with no retinopathy, or non-STDR at baseline in one/both eyes, were used to calculate STDR incidence rates by sociodemographic factors, diabetes type, and duration. HR from Cox models examined associations with STDR.

RESULTS

There were 16 388 incident STDR cases over a median of 5.4 years (IQR 2.8-8.2; STDR rate 2.214, 95% CI 2.214 to 2.215 per 100 person-years). People with no retinopathy at baseline had a lower risk of sight-threatening diabetic retinopathy (STDR) compared with those with non-STDR in one eye (HR 3.03, 95% CI 2.91 to 3.15, p<0.001) and both eyes (HR 7.88, 95% CI 7.59 to 8.18, p<0.001). Black and South Asian individuals had higher STDR hazards than white individuals (HR 1.57, 95% CI 1.50 to 1.64 and HR 1.36, 95% CI 1.31 to 1.42, respectively). Additionally, every 5-year increase in age at inclusion was associated with an 8% reduction in STDR hazards (p<0.001).

CONCLUSIONS

Ethnic disparities exist in a health system limited by capacity rather than patient economic circumstances. Diabetic retinopathy at first screen is a strong determinant of STDR development. By using basic demographic characteristics, screening programmes or clinical practices can stratify risk for sight-threatening diabetic retinopathy development.

Novel Outer Retinal Columnar Abnormalities (ORCA) and Non-Vasogenic Cystoid Macular Edema in Dense Deposit Disease

PURPOSE

To describe the occurrence of bilateral outer retinal columnar abnormalities, non-vasogenic cystoid macular edema, and drusen in the context of dense deposit disease.

METHODS

Case report.

PATIENT

An 18-year-old female with dense deposit disease was referred to our specialist center for diagnosis and management with findings consistent with bilateral non-vasogenic cystoid macular edema and drusen. She was followed up in our clinic for forty months and treated with acetazolamide and ketorolac drops.

RESULTS

Baseline examination revealed bilateral visual acuity (VA) reduction, and macular elevation with peripapillary drusen on fundus biomicroscopy. Optical coherence tomography revealed bilateral hyporeflective cystoid central macula changes, microcystoid changes with increased central subfield thickness (>450 microns), and outer retinal columnar abnormalities (ORCAs). Fluorescein angiography showed no evidence of macular leakage. Electrodiagnostic testing was within normal limits. Over the course of follow-up, she received treatment with acetazolamide 250mg BD PO and ketorolac 0.5% eye drops, with a partial reduction in her edema and improvement in VA.

CONCLUSION

Dense deposit disease is a rare disease secondary to complement cascade dysregulation, associated with drusen. To the best of our knowledge, this is the first report of bilateral non-vasogenic cystoid macular edema and ORCA in a young female patient with dense deposit disease, confirmed with multimodal imaging.

Ethnicity is not biology: retinal pigment score to evaluate biological variability from ophthalmic imaging using machine learning

Background

Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as an inappropriate marker for biological variability.

Methods

We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study).

Findings

A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which 8 were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores.

Interpretation

RPS serves to decouple traditional demographic variables, such as ethnicity, from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score.

Funding

The authors did not receive support from any organisation for the submitted work.

Divergent amino acid and sphingolipid metabolism in patients with inherited neuro-retinal disease

OBJECTIVES

The non-essential amino acids serine, glycine, and alanine, as well as diverse sphingolipid species, are implicated in inherited neuro-retinal disorders and are metabolically linked by serine palmitoyltransferase (SPT), a key enzyme in membrane lipid biogenesis. To gain insight into the pathophysiological mechanisms linking these pathways to neuro-retinal diseases we compared patients diagnosed with two metabolically intertwined diseases: macular telangiectasia type II (MacTel), hereditary sensory autonomic neuropathy type 1 (HSAN1), or both.

METHODS

We performed targeted metabolomic analyses of amino acids and broad sphingolipids in sera from a cohort of MacTel (205), HSAN1 (25) and Control (151) participants.

RESULTS

MacTel patients exhibited broad alterations of amino acids, including changes in serine, glycine, alanine, glutamate, and branched-chain amino acids reminiscent of diabetes. MacTel patients had elevated 1-deoxysphingolipids but reduced levels of complex sphingolipids in circulation. A mouse model of retinopathy indicates dietary serine and glycine restriction can drive this depletion in complex sphingolipids. HSAN1 patients exhibited elevated serine, lower alanine, and a reduction in canonical ceramides and sphingomyelins compared to controls. Those patients diagnosed with both HSAN1 and MacTel showed the most significant decrease in circulating sphingomyelins.

CONCLUSIONS

These results highlight metabolic distinctions between MacTel and HSAN1, emphasize the importance of membrane lipids in the progression of MacTel, and suggest distinct therapeutic approaches for these two neurodegenerative diseases.

Training Deep Learning Models to Work on Multiple Devices by Cross-Domain Learning with No Additional Annotations

PURPOSE

To create an unsupervised cross-domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic macular OCT images from different manufacturers and camera devices.

DESIGN

We sought to use generative adversarial networks (GANs) to generalize a segmentation model trained on one OCT device to segment B-scans obtained from a different OCT device manufacturer in a fully unsupervised approach without labeled data from the latter manufacturer.

PARTICIPANTS

A total of 732 OCT B-scans from 4 different OCT devices (Heidelberg Spectralis, Topcon 1000, Maestro2, and Zeiss Plex Elite 9000).

METHODS

We developed an unsupervised GAN model, GANSeg, to segment 7 retinal layers and intraretinal fluid in Topcon 1000 OCT images (domain B) that had access only to labeled data on Heidelberg Spectralis images (domain A). GANSeg was unsupervised because it had access only to 110 Heidelberg labeled OCTs and 556 raw and unlabeled Topcon 1000 OCTs. To validate GANSeg segmentations, 3 masked graders manually segmented 60 OCTs from an external Topcon 1000 test dataset independently. To test the limits of GANSeg, graders also manually segmented 3 OCTs from Zeiss Plex Elite 9000 and Topcon Maestro2. A U-Net was trained on the same labeled Heidelberg images as baseline. The GANSeg repository with labeled annotations is at https://github.com/uw-biomedical-ml/ganseg.

MAIN OUTCOME MEASURES

Dice scores comparing segmentation results from GANSeg and the U-Net model with the manual segmented images.

RESULTS

Although GANSeg and U-Net achieved comparable Dice scores performance as human experts on the labeled Heidelberg test dataset, only GANSeg achieved comparable Dice scores with the best performance for the ganglion cell layer plus inner plexiform layer (90%; 95% confidence interval [CI], 68%-96%) and the worst performance for intraretinal fluid (58%; 95% CI, 18%-89%), which was statistically similar to human graders (79%; 95% CI, 43%-94%). GANSeg significantly outperformed the U-Net model. Moreover, GANSeg generalized to both Zeiss and Topcon Maestro2 swept-source OCT domains, which it had never encountered before.

CONCLUSIONS

GANSeg enables the transfer of supervised deep learning algorithms across OCT devices without labeled data, thereby greatly expanding the applicability of deep learning algorithms.

A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography

Purpose

The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans.

Methods

A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), kappa, accuracy, intraclass correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curves.

Results

The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification models). The mean ICC for the drusen and RPD area versus graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance.

Conclusions

The models achieved high classification and segmentation performance, similar to human performance.

Translational Relevance

Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings.

Quantification and Predictors of OCT-Based Macular Curvature and Dome-Shaped Configuration: Results From the UK Biobank

Purpose

To investigate macular curvature, including the evaluation of potential associations and the dome-shaped macular configuration, given the increasing myopia prevalence and expected associated macular malformations.

Methods

The study included a total of 65,440 subjects with a mean age (± SD) of 57.3 ± 8.11 years with spectral-domain optical coherence tomography (OCT) data from a unique contemporary resource for the study of health and disease that recruited more than half a million people in the United Kingdom (UK Biobank). A deep learning model was used to segment the retinal pigment epithelium. The macular curvature of the OCT scans was calculated by polynomial fit and evaluated. Further, associations with demographic, functional, ocular, and infancy factors were examined.

Results

The overall macular curvature values followed a Gaussian distribution with high inter-eye agreement. Although all of the investigated parameters, except maternal smoking, were associated with the curvature in a multilinear analysis, ethnicity and refractive error consistently revealed the most significant effect. The prevalence of a macular dome-shaped configuration was 4.8% overall, most commonly in Chinese subjects as well as hypermetropic eyes. An increasing frequency up to 22.0% was found toward high refractive error. Subretinal fluid was rarely found in these eyes.

Conclusions

Macular curvature revealed associations with demographic, functional, ocular, and infancy factors, as well as increasing prevalence of a dome-shaped macular configuration in high refractive error including high myopia and hypermetropia. These findings imply different pathophysiologic processes that lead to macular development and might open new fields to future myopia and macula research.

Description of a patient cohort with Hereditary Sensory Neuropathy Type 1 without retinal disease Macular Telangiectasia type 2 - implications for retinal screening in HSN1

BACKGROUND AND AIMS

Pathogenic variants in the genes encoding serine palmitoyl transferase (SPTLC1 or SPTLC2) are the most common causes of the rare peripheral nerve disorder Hereditary Sensory Neuropathy Type 1 (HSN1). Macular telangiectasia type 2 (MacTel), a retinal disorder associated with disordered serine-glycine metabolism and has been described in some patients with HSN1. This study aims to further investigate this association in a cohort of people with HSN1.

METHODS

Fourteen patients with a clinically and genetically confirmed diagnosis of HSN1 from the National Hospital for Neurology and Neurosurgery (NHNN, University College London Hospitals NHS Foundation Trust, London, United Kingdom) were recruited to the MacTel Registry, between July 2018 and April 2019. Two additional patients were identified from the dataset of the international clinical registry study (www.lmri.net). Ocular examination included fundus autofluorescence, blue light and infrared reflectance, macular pigment optical density mapping, and optical coherence tomography.

RESULTS

Twelve patients had a pathogenic variant in the SPTLC1 gene, with p.Cys133Trp in eleven cases (92%) and p.Cys133Tyr in one case (8%). Four patients had a variant in the SPTLC2 gene. None of the patients showed clinical evidence of MacTel.

INTERPRETATION

The link between HSN1 and MacTel seems more complex than can solely be explained by the genetic variants. An extension of the spectrum of SPTLC1/2-related disease with phenotypic pleiotropy is proposed. HSN1 patients should be screened for visual symptoms and referred for specialist retinal screening, but the association of the two diseases is likely to be variable and remains unexplained. This article is protected by copyright. All rights reserved.

Warwick A, Müller PL, Williams KM, Rudnicka AR, Owen CG, Lee AY, Egan C, Tufail A. Foveal Curvature and Its Associations in UK Biobank Participants

Purpose

To examine whether sociodemographic, and ocular factors relate to optical coherence tomography (OCT)-derived foveal curvature (FC) in healthy individuals.

Methods

We developed a deep learning model to quantify OCT-derived FC from 63,939 participants (age range, 39-70 years). Associations of FC with sociodemographic, and ocular factors were obtained using multilevel regression analysis (to allow for right and left eyes) adjusting for age, sex, ethnicity, height (model 1), visual acuity, spherical equivalent, corneal astigmatism, center point retinal thickness (CPRT), intraocular pressure (model 2), deprivation (Townsend index), higher education, annual income, and birth order (model 3). Fovea curvature was modeled as a z-score.

Results

Males had on average steeper FC (0.077; 95% confidence interval [CI] 0.077-0.078) than females (0.068; 95% CI 0.068-0.069). Compared with whites, non-white individuals showed flatter FC, particularly those of black ethnicity. In black males, -0.80 standard deviation (SD) change when compared with whites (95% CI -0.89, -0.71; P 5.2e10-68). In black females, -0.70 SD change when compared with whites (95% CI -0.77, -0.63; p 2.3e10-93). Ocular factors (visual acuity, refractive status, and CPRT) showed a graded inverse association with FC that persisted after adjustment. Macular curvature showed a positive association with FC. Income showed a linear trend increase in males (P for linear trend = 0.005).

Conclusions

We demonstrate marked differences in FC with ethnicity on the largest cohort studied for this purpose to date. Ocular factors showed a graded association with FC. Implementation of FC quantification in research and on the clinical setting can enhance the understanding of clinical macular phenotypes in health and disease.

Estimating excess visual loss from neovascular age-related macular degeneration in the UK during the COVID-19 pandemic: a retrospective clinical audit and simulation model

OBJECTIVES

To report the reduction in new neovascular age-related macular degeneration (nAMD) referrals during the COVID-19 pandemic and estimate the impact of delayed treatment on visual outcomes at 1 year.

DESIGN

Retrospective clinical audit and simulation model.

SETTING

Multiple UK National Health Service (NHS) ophthalmology centres.

PARTICIPANTS

Data on the reduction in new nAMD referrals were obtained from four NHS Trusts comparing April 2020 with April 2019. To estimate the potential impact on 1-year visual outcomes, a stratified bootstrap simulation model was developed drawing on an electronic medical records dataset of 20 825 nAMD eyes from 27 NHS Trusts.

MAIN OUTCOME MEASURES

Simulated mean visual acuity and proportions of eyes with vision ≤6/60, ≤6/24 and ≥6/12 at 1 year under four hypothetical scenarios: 0-month, 3-month, 6-month and 9-month treatment delays. Estimated additional number of eyes with vision ≤6/60 at 1 year nationally.

RESULTS

The number of nAMD referrals dropped on average by 72% (range 65%-87%). Simulated 1-year visual outcomes for 1000 nAMD eyes with a 3-month treatment delay suggested an increase in the proportion of eyes with vision ≤6/60 from 15.5% (13.2%-17.9%) to 23.3% (20.7%-25.9%), and a decrease in the proportion of eyes with vision ≥6/12 (driving vision) from 35.1% (32.1%-38.1%) to 26.4% (23.8%-29.2%). Outcomes worsened incrementally with longer modelled delays. Assuming nAMD referrals are reduced to this level for 1 month nationally, these simulated results suggest an additional 186-365 eyes with vision ≤6/60 at 1 year.

CONCLUSIONS

We report a large decrease in nAMD referrals during the COVID-19 lockdown and provide an important public health message regarding the risk of delayed treatment. As a conservative estimate, a treatment delay of 3 months could lead to a >50% relative increase in the number of eyes with vision ≤6/60 and 25% relative decrease in the number of eyes with driving vision at 1 year.

Elongin C (ELOC/TCEB1)-associated von Hippel-Lindau disease

Around 95% of patients with clinical features that meet the diagnostic criteria for von Hippel-Lindau disease (VHL) have a detectable inactivating germline variant in VHL. The VHL protein (pVHL) functions as part of the E3 ubiquitin ligase complex comprising pVHL, elongin C, elongin B, cullin 2 and ring box 1 (VCB-CR complex), which plays a key role in oxygen sensing and degradation of hypoxia-inducible factors. To date, only variants in VHL have been shown to cause VHL disease. We undertook trio analysis by whole-exome sequencing in a proband with VHL disease but without a detectable VHL mutation. Molecular studies were also performed on paired DNA extracted from the proband's kidney tumour and blood and bioinformatics analysis of sporadic renal cell carcinoma (RCC) dataset was undertaken. A de novo pathogenic variant in ELOC NM_005648.4(ELOC):c.236A>G (p.Tyr79Cys) gene was identified in the proband. ELOC encodes elongin C, a key component [C] of the VCB-CR complex. The p.Tyr79Cys substitution is a mutational hotspot in sporadic VHL-competent RCC and has previously been shown to mimic the effects of pVHL deficiency on hypoxic signalling. Analysis of an RCC from the proband showed similar findings to that in somatically ELOC-mutated RCC (expression of hypoxia-responsive proteins, no somatic VHL variants and chromosome 8 loss). These findings are consistent with pathogenic ELOC variants being a novel cause for VHL disease and suggest that genetic testing for ELOC variants should be performed in individuals with suspected VHL disease with no detectable VHL variant.

Reference database of total retinal vessel surface area derived from volume-rendered optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) enables three-dimensional, high-resolution, depth-resolved flow to be distinguished from non-vessel tissue signals in the retina. Thus, it enables the quantification of the 3D surface area of the retinal vessel signal. Despite the widespread use of OCTA, no representative spatially rendered reference vessel surface area data are published. In this study, the OCTA vessel surface areas in 203 eyes of 107 healthy participants were measured in the 3D domain. A Generalized Linear Model (GLM) model analysis was performed to investigate the effects of sex, age, spherical equivalent, axial length, and visual acuity on the OCTA vessel surface area. The mean overall vessel surface area was 54.53 mm² (range from 27.03 to 88.7 mm²). OCTA vessel surface area was slightly negatively correlated with age. However, the GLM model analysis identified axial length as having the strongest effect on OCTA vessel surface area. No significant correlations were found for sex or between left and right eyes. This is the first study to characterize three-dimensional vascular parameters in a population based on OCTA with respect to the vessel surface area.

Feasibility and tolerability of ophthalmic virtual reality as a medical communication tool in children and young people

PURPOSE

Virtual reality (VR) can be useful in explaining diseases and complications that affect children in order to improve medical communications with this vulnerable patient group. So far, children and young people's responses to high-end medical VR environments have never been assessed.

METHODS

An unprecedented number of 320 children and young people were given the opportunity to interact with a VR application displaying original ophthalmic volume data via a commercially available tethered head-mounted display (HMD). Participants completed three surveys: demographics and experience with VR, usability and perceived utility of this technology and the Simulator Sickness Questionnaire. The second survey also probed participants for suggestions on improvements and whether this system could be useful for increasing engagement in science.

RESULTS

A total of 206 sets of surveys were received. 165 children and young people (84 female) aged 12-18 years (mean, 15 years) completed surveys that could be used for analysis. 69 participants (47.59%) were VR-naïve, and 76 (52.41%) reported that they had previous VR experience. Results show that VR facilitated understanding of ophthalmological complications and was reasonably tolerated. Lastly, exposure to VR raised children and young people's awareness and interest in science.

CONCLUSIONS

The VR platform used was successfully utilized and was well accepted in children to display and interact with volume-rendered 3D ophthalmological data. Virtual reality (VR) is suitable as a novel image display platform in ophthalmology to engage children and young people.

Dynamic volume-rendered optical coherence tomography pupillometry

PURPOSE

To assess intrapupillary space (IPS) changes in healthy subjects with regard to decreased iris motility in patients with pseudoexfoliation glaucoma (PEXG) or non-arteritic anterior ischaemic optic neuropathy (NAION) in a feasibility study in a clinical environment.

METHODS

Scotopic and photopic IPS measurements using three-dimensionally rendered swept-source optical coherence tomography (SS-OCT) data were obtained and compared for all subjects. Intrapupillary space (IPS) parameters were evaluated such as absolute volumetric differences, relative light response for volumetric ratios and pupillary ejection fraction (PEF) for functional contraction measurements.

RESULTS

From a total of 122 IPS from 66 subjects, 106 IPS were eligible for comparison providing values for 72 normal, 30 PEXG and 4 NAION eyes. In healthy, PEXG and NAION subjects, scotopic overall mean IPS was 8.90, 3.45 and 4.16 mm³ , and photopic overall mean IPS was 0.87, 0.74 and 1.13 mm³ , respectively. Three-dimensional contractility showed a mean absolute difference of 8.03 mm³ for normals (defined as 100% contractility), 2.72 mm³ for PEXG (33.88% of normal) and 3.03 mm³ for NAION (38.50% of normal) with a relative light response ratio between scotopic and photopic volumes of 10.26 (100%), 4.69 (45.70%) and 3.67 (35.78%), respectively. Pupillary ejection fraction (PEF) showed a contractile pupillary emptying of 88.11% for normals, 76.92% for PEXG and 70.91% for NAION patients.

CONCLUSION

This 3D pupillometry OCT assessment allows for quantitative measurements of pupil function, contractility and response to light. More specifically, PEF is presented as a potential (neuro)-pupillary outcome measure that could be useful in the monitoring of ophthalmic disorders that affect pupillary function.

Multicentre study of 4626 patients assesses the effectiveness, safety and burden of two categories of treatments for central retinal vein occlusion: intravitreal anti-vascular endothelial growth factor injections and intravitreal Ozurdex injections

BACKGROUND/AIMS

To assess the effectiveness, burden and safety of two categories of treatment for central retinal vein occlusion (CRVO): intravitreal injections of anti-vascular endothelial growth factor (anti-VEGF) and dexamethasone (Ozurdex).

METHODS

A retrospective analysis of Medisoft electronic medical record (EMR) data from 27 National Health Service sites in the UK identified 4626 treatment-naive patients with a single mode of treatment for macular oedema secondary to CRVO. Statistics describing the overall CRVO patient cohort and individual patient subpopulations stratified by treatment type were generated. Mean age at baseline, gender, ethnicity, social deprivation and visual acuity (VA) follow-up was reported. Absolute and change in VA using ETDRS are used to describe treatment effectiveness, the number of injections and visits used to describe treatment burden and endophthalmitis rates as a marker of treatment safety.

RESULTS

Mean VA was 47.9 and 45.3 EDTRS letters in the anti-VEGF and Ozurdex groups, respectively. This changed to 57.9/53.7 at 12 months, 58.3/46.9 at 18 months and 59.4/51.0 at 36 months. Mean number of injections were 5.6/1.6 at 12 months, 6.0/1.7 at 18 months and 7.0/1.8 at 36 months. Endophthalmitis rates were 0.003% (n=4) for the anti-VEGF group and 0.09% (n=1) for the Ozurdex group.

CONCLUSIONS

VA improvements were greater and more sustained with anti-VEGF treatment. Lower starting acuity resulted in bigger gains in both groups, while higher starting acuity resulted in higher VA at 36 months. Although treatment burden was greater with anti-VEGF, Ozurdex was associated with higher rates of endophthalmitis.