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.

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.

Two-year recall for people with no diabetic retinopathy: a multi-ethnic population-based retrospective cohort study using real-world data to quantify the effect.

BACKGROUND/AIMS: The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual screening. Less frequent screening has been advocated among PLD without diabetic retinopathy (DR), but evidence for each ethnic group is limited. We examined the potential effect of biennial versus annual screening on the detection of sight-threatening diabetic retinopathy (STDR) and proliferative diabetic retinopathy (PDR) among PLD without DR from a large urban multi-ethnic English DESP. METHODS: PLD in North-East London DESP (January 2012 to December 2021) with no DR on two prior consecutive screening visits with up to 8 years of follow-up were examined. Annual STDR and PDR incidence rates, overall and by ethnicity, were quantified. Delays in identification of STDR and PDR events had 2-year screening intervals been used were determined. FINDINGS: Among 82 782 PLD (37% white, 36% South Asian, and 16% black people), there were 1788 incident STDR cases over mean (SD) 4.3 (2.4) years (STDR rate 0.51, 95% CI 0.47 to 0.55 per 100-person-years). STDR incidence rates per 100-person-years by ethnicity were 0.55 (95% CI 0.48 to 0.62) for South Asian, 0.34 (95% CI 0.29 to 0.40) for white, and 0.77 (95% CI 0.65 to 0.90) for black people. Biennial screening would have delayed diagnosis by 1 year for 56.3% (1007/1788) with STDR and 43.6% (45/103) with PDR. Standardised cumulative rates of delayed STDR per 100 000 persons for each ethnic group were 1904 (95% CI 1683 to 2154) for black people, 1276 (95% CI 1153 to 1412) for South Asian people, and 844 (95% CI 745 to 955) for white people. INTERPRETATION: Biennial screening would have delayed detection of some STDR and PDR by 1 year, especially among those of black ethnic origin, leading to healthcare inequalities.

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.

Formal registration of visual impairment in people with diabetic retinopathy significantly underestimates the scale of the problem: a retrospective cohort study at a tertiary care eye hospital service in the UK.

AIMS: To analyse the prevalence of visual impairment (VI), compare it to certification of visual impairment (CVI) and analyse VI associations in patients with diabetic retinopathy (DR). METHODS: Retrospective cohort study, which included 8007 patients with DR referred from the English diabetic eye screening programme to a tertiary referral eye hospital. Main outcome measure was VI, defined as vision in the best eye of <6/24. We conducted a multivariable logistic regression for VI as primary outcome of interest, controlling for age, sex, type of diabetes, baseline DR grade, ethnicity and index of multiple deprivation (IMD). RESULTS: Mean age was 64.5 (SD 13.6) years; 61% of patients were men; and 31% of South Asian ethnicity. There were 68 patients with CVI during the study period, and 84% (272/325) of patients with VI did not have CVI after a mean follow-up of 1.87 (SD ±0.86) years. Older age showed a positive association with VI (OR per decade rise 1.88, 95% CI 1.70 to 2.08; p=1.8×10-34). Men had a lower risk of VI (OR 0.62, 95% CI 0.50 to 0.79, p=6.0×10-5), and less deprivation had a graded inverse association with VI (OR per IMD category increase 0.83, 95% CI 0.74 to 0.93, p value for linear trend 0.002). CONCLUSION: The majority of people with vision impairment are not registered at the point of care, which could translate to underestimation of diabetes-related VI and all-cause VI at a national level if replicated at other centres. Further work is needed to explore rates of VI and uptake of registration.

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.

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.

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.

Simultaneous and Bilateral Non-Arteritic Anterior Ischaemic Optic Neuropathy and Acute Angle-Closure.

Non-arteritic anterior ischaemic optic neuropathy (NAION) is the second most common cause of permanent optic nerve-related visual loss in adults after glaucoma. NAION is caused by complex mechanisms that lead to optic nerve head hypoperfusion and is frequently associated with cardiovascular risk factors like type 2 diabetes mellitus (DM2) and hypertension. An attack of acute angle-closure (AAC) occurs when the trabecular meshwork is blocked with peripheral iris that causes an abrupt rise in intraocular pressure, which can trigger a decrease in optic nerve head perfusion. We present a case with simultaneous and bilateral AAC and NAION in association with uncontrolled DM2.

Effect of ethnicity and other sociodemographic factors on attendance at diabetic eye screening: a 12-month retrospective cohort study.

OBJECTIVES: To examine the association of sociodemographic characteristics with attendance at diabetic eye screening in a large ethnically diverse urban population. DESIGN: Retrospective cohort study. SETTING: Screening visits in the North East London Diabetic Eye Screening Programme (NELDESP). PARTICIPANTS: 84 449 people with diabetes aged 12 years or older registered in the NELDESP and scheduled for screening between 1 April 2017 and 31 March 2018. MAIN OUTCOME MEASURE: Attendance at diabetic eye screening appointments. RESULTS: The mean age of people with diabetes was 60 years (SD 14.2 years), 53.4% were men, 41% South Asian, 29% White British and 17% Black; 83.4% attended screening. Black people with diabetes had similar levels of attendance compared with White British people. However, South Asian, Chinese and 'Any other Asian' background ethnicities showed greater odds of attendance compared with White British. When compared with their respective reference group, high levels of deprivation, younger age, longer duration of diabetes and worse visual acuity, were all associated with non-attendance. There was a higher likelihood of attendance per quintile improvement in deprivation (OR, 1.06; 95% CI, 1.03 to 1.08), with increasing age (OR per decade, 1.17; 95% CI, 1.15 to 1.19), with better visual acuity (OR per Bailey-Lovie chart line 1.12; 95% CI, 1.11 to 1.14) and with longer time of NELDESP registration (OR per year, 1.02; 95% CI, 1.01 to 1.03). CONCLUSION: Ethnic differences in diabetic eye screening uptake, though small, are evident. Despite preconceptions, a higher likelihood of screening attendance was observed among Asian ethnic groups when compared with the White ethnic group. Poorer socioeconomic profile was associated with higher likelihood of non-attendance for screening. Further work is needed to understand how to target individuals at risk of non-attendance and reduce inequalities.

Reliability of Retinal Pathology Quantification in Age-Related Macular Degeneration: Implications for Clinical Trials and Machine Learning Applications.

Purpose: To investigate the interreader agreement for grading of retinal alterations in age-related macular degeneration (AMD) using a reading center setting. Methods: In this cross-sectional case series, spectral-domain optical coherence tomography (OCT; Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovascular AMD (56 treatment naive, 56 after three anti-vascular endothelial growth factor injections) were analyzed by four independent readers. Imaging features specific for AMD were annotated using a novel custom-built annotation platform. Dice score, Bland-Altman plots, coefficients of repeatability, coefficients of variation, and intraclass correlation coefficients were assessed. Results: Loss of ellipsoid zone, pigment epithelium detachment, subretinal fluid, and drusen were the most abundant features in our cohort. Subretinal fluid, intraretinal fluid, hypertransmission, descent of the outer plexiform layer, and pigment epithelium detachment showed highest interreader agreement, while detection and measures of loss of ellipsoid zone and retinal pigment epithelium were more variable. The agreement on the size and location of the respective annotation was more consistent throughout all features. Conclusions: The interreader agreement depended on the respective OCT-based feature. A selection of reliable features might provide suitable surrogate markers for disease progression and possible treatment effects focusing on different disease stages. Translational Relevance: This might give opportunities for a more time- and cost-effective patient assessment and improved decision making as well as have implications for clinical trials and training machine learning algorithms.

Evolving Treatment Patterns and Outcomes of Neovascular Age-Related Macular Degeneration Over a Decade.

PURPOSE: Management of neovascular age-related macular degeneration (nAMD) has evolved over the last decade with several treatment regimens and medications. This study describes the treatment patterns and visual outcomes over 10 years in a large cohort of patients. DESIGN: Retrospective analysis of electronic health records from 27 National Health Service secondary care healthcare providers in the UK. PARTICIPANTS: Treatment-naïve patients receiving at least 3 intravitreal anti-vascular endothelial growth factor (VEGF) injections for nAMD in their first 6 months of follow-up were included. Patients with missing data for age or gender and those aged less than 55 years were excluded. METHODS: Eyes with at least 3 years of follow-up were grouped by years of treatment initiation, and 3-year outcomes were compared between the groups. Data were generated during routine clinical care between September 2008 and December 2018. MAIN OUTCOME MEASURES: Visual acuity (VA), number of injections, and number of visits. RESULTS: A total of 15 810 eyes of 13 705 patients receiving 195 104 injections were included. Visual acuity improved from baseline during the first year, but decreased thereafter, resulting in loss of visual gains. This trend remained consistent throughout the past decade. Although an increasing proportion of eyes remained in the driving standard, this was driven by better presenting VA over the decade. The number of injections decreased substantially between the first and subsequent years, from a mean of 6.25 in year 1 to 3 in year 2 and 2.5 in year 3, without improvement over the decade. In a multivariable regression analysis, final VA improved by 0.24 letters for each year since 2008, and younger age and baseline VA were significantly associated with VA at 3 years. CONCLUSIONS: Our findings show that despite improvement in functional VA over the years, primarily driven by improving baseline VA, patients continue to lose vision after the first year of treatment, with only marginal change over the past decade. The data suggest these results may be related to suboptimal treatment patterns, which have not improved over the years. Rethinking treatment strategies may be warranted, possibly on a national level or through the introduction of longer-acting therapies.

Contextualizing single-arm trials with real-world data: An emulated target trial comparing therapies for neovascular age-related macular degeneration.

One-in-four ophthalmology trials are single-armed, which poses challenges to their interpretation. We demonstrate how real-world cohorts used as external/synthetic control arms can contextualize such trials. We herein emulated a target trial on the intention-to-treat efficacy of off-label bevacizumab (q6w) pro re nata relative to fixed-interval aflibercept (q8w) for improving week 54 visual acuity of eyes affected by neovascular age-related macular degeneration. The bevacizumab arm (n = 65) was taken from the ABC randomized controlled trial. A total of 4,471 aflibercept-treated eyes aligning with the ABC trial eligibility were identified from electronic health records and synthetic control arms were created by emulating randomization conditional on age, sex, and baseline visual read via exact matching and propensity score methods. We undertook an inferiority analysis on mean difference at 54 weeks; outcomes regression on achieving a change in visual acuity of greater than or equal to 15, greater than or equal to 10, and less than or equal to -15 Early Treatment Diabetic Retinopathy (ETDRS) letters at week 54; and a time-to-event analysis on achieving a change in visual acuity of greater than or equal to 15, greater than or equal to 10, and less than or equal to -15 ETDRS letters by week 54. The findings suggest off-label bevacizumab to be neither inferior nor superior to licensed aflibercept. Our study highlights how real-world cohorts representing the counterfactual intervention could aid the interpretation of single-armed trials when analyzed in accord to the target trial framework. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? One-in-four randomized controlled trials in ophthalmology are single-armed, which poses challenges for interpreting their efficacy relative to standard of care. Recent conceptual advances in the methods of causal inference and in the emulation of target trials suggests that the standard-of-care arms representing the counterfactual intervention can be approximated with observational data. WHAT QUESTION DID THIS STUDY ADDRESS? How real-world cohorts representing the counterfactual intervention can aid the interpretation of single-armed ophthalmological trials. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? Our study highlights how real-world cohorts representing the counterfactual intervention could aid the interpretation of single-armed ophthalmological trials when undertaken in accord with the target trial framework. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? External counterfactual arms could reduce the time and cost to reach potential regulatory approval.

Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning.

PURPOSE: We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD). DESIGN: Development and validation of a deep-learning model for feature segmentation. METHODS: Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve. RESULTS: On 11 of 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared with 0.61 ± 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 ± 0.22, compared with 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers. CONCLUSIONS: The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.

Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.

BACKGROUND: Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading. METHODS: Cross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images. RESULTS: We included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images. CONCLUSION: EyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of 'no retinopathy' and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting.