Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology.

Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. 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) and reproduced in a Tanzanian, an Australian, and a Chinese dataset.

Patient and practitioner perceptions around use of artificial intelligence within the English NHS diabetic eye screening programme.

Aims: Automated retinal image analysis using Artificial Intelligence (AI) can detect diabetic retinopathy as accurately as human graders, but it is not yet licensed in the NHS Diabetic Eye Screening Programme (DESP) in England. This study aims to assess perceptions of People Living with Diabetes (PLD) and Healthcare Practitioners (HCP) towards AI's introduction in DESP.

Methods: Two online surveys were co-developed with PLD and HCP from a diverse DESP in North East London.

Spontaneous suprachoroidal haemorrhage: clinical features, visual outcomes and prognosis factors.

Background: To describe clinical features, risk factors and outcomes of patients with diagnosis of rare spontaneous suprachoroidal haemorrhage (SSCH) over a 20-year period from a tertiary eye unit.

Methods: Retrospective, observational case-series of patients with SSCH, defined as SCH without a known cause at diagnosis. Variables analysed included age, gender, ethnicity, systemic and ocular comorbidities, systemic medication, initial and final best corrected visual acuity (BCVA), clinical features, management and follow-up.

Deciphering the Connection Between Microvascular Damage and Neurodegeneration in Early Diabetic Retinopathy.

Diabetic retinopathy (DR), a common diabetes complication leading to vision loss, presents early clinical signs linked to retinal vasculature damage, affecting the neural retina at advanced stages. However, vascular changes and potential effects on neural cells before clinical diagnosis of DR are less well understood. To study the earliest stages of DR, we performed histological phenotyping and quantitative analysis on postmortem retinas from 10 donors with diabetes and without signs of DR (e.

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.

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.

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.

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.

NOVEL OUTER RETINAL COLUMNAR ABNORMALITIES AND NONVASOGENIC CYSTOID MACULAR EDEMA IN DENSE DEPOSIT DISEASE.

Purpose: To describe the occurrence of bilateral outer retinal columnar abnormalities, nonvasogenic cystoid macular edema, and drusen in the context of dense deposit disease.

Methods: Case report.

Patient: An 18-year-old girl with dense deposit disease was referred to our specialist center for diagnosis and management with findings consistent with bilateral nonvasogenic cystoid macular edema and drusen.

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).

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).

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.

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).

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.

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).

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.

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.

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).

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.

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.

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.

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.

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.

Comparison of true-colour wide-field confocal scanner imaging with standard fundus photography for diabetic retinopathy screening.

Background: Screening of diabetic retinopathy (DR) reduces blindness by early identification of retinopathy. This study compares DR grades derived from a two-field imaging protocol from two imaging platforms, one providing a single 60-degree horizontal field of view (FOV) and the other, a standard 45-degree FOV.

Methods: Cross-sectional study which included 1257 diabetic patients aged ≥18 years attending their DR screening visit in the English National Diabetic Eye Screening Programme (NDESP).