Primary rhegmatogenous retinal detachment: clinical epidemiology and genetic aetiology

Primary rhegmatogenous retinal detachment (RRD) is one of the most common ophthalmic emergencies. RRD is caused by a full thickness break in the retina which initiates separation of the neurosensory retina from the underlying retinal pigment epithelium. The subsequent accumulation of fluid within this potential space extends the area of detachment and causes visual loss. Previous assessments of RRD incidence have demonstrated large differences in case definition and methodology, with incidence estimates varying 3-fold geographically and in different time periods. To date there have been no systematic or prospective incidence estimates of primary RRD in the U.K. In this thesis I present the findings of a 2-year epidemiology study that prospectively aimed to recruit all incident cases of primary RRD diagnosed in Scotland. Case recruitment from consenting participants comprised a detailed questionnaire and a blood sample. In this thesis, I present the findings of the Scottish retinal detachment study that examined the incidence, demographic features, temporal incidence trends, as well as clinical and socio-economic associations of primary RRD in Scotland. From the clinical and genetic resource I assembled, I calculated the first population based estimate of the sibling recurrence risk ratio for RRD and designed and assisted in the analysis of the first case-control genome wide association study of this condition. Results from this study have estimated the annual incidence of primary RRD in Scotland to be 12.05 per 100,000 population. Based on this estimate, there are approximately 7,300 new cases annually in the United Kingdom. RRD incidence increases with age, is more common in men and right eyes, and is strongly associated with socio-economic affluence. In addition, using hospital episode data, the overall age-standardised incidence of RRD in Scotland was shown to be steadily increasing since 1987 with an average annual increase of 1.9%. Analysis of the clinical findings highlighted that the majority of RRD cases are caused by more than one retinal break; an important consideration for appropriate surgical management. Ocular trauma, previous cataract surgery, family history, and retinal degeneration are important predisposing features. In addition, over a 2 year period approximately 7% of individuals will suffer a RRD in the fellow eye representing an important risk of bilateral visual loss. Furthermore, I demonstrate that the risk of having an affected sibling with RRD is increased 2-fold given that one sibling has had the condition, substantiating a genetic component to the pathogenesis of this condition. In the final aspect of this thesis I will present the design and analysis of a two stage case-control genome-wide association study examining the role of common genetic variants and selected candidate genes in predisposing to RRD development

Crowdsourcing as a novel technique for retinal fundus photography classification: analysis of images in the EPIC Norfolk cohort on behalf of the UK Biobank Eye and Vision Consortium.

AIM: Crowdsourcing is the process of outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing for the classification of retinal fundus photography. METHODS: One hundred retinal fundus photograph images with pre-determined disease criteria were selected by experts from a large cohort study. After reading brief instructions and an example classification, we requested that knowledge workers (KWs) from a crowdsourcing platform classified each image as normal or abnormal with grades of severity. Each image was classified 20 times by different KWs. Four study designs were examined to assess the effect of varying incentive and KW experience in classification accuracy. All study designs were conducted twice to examine repeatability. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC). RESULTS: Without restriction on eligible participants, two thousand classifications of 100 images were received in under 24 hours at minimal cost. In trial 1 all study designs had an AUC (95%CI) of 0.701(0.680-0.721) or greater for classification of normal/abnormal. In trial 1, the highest AUC (95%CI) for normal/abnormal classification was 0.757 (0.738-0.776) for KWs with moderate experience. Comparable results were observed in trial 2. In trial 1, between 64-86% of any abnormal image was correctly classified by over half of all KWs. In trial 2, this ranged between 74-97%. Sensitivity was ≥ 96% for normal versus severely abnormal detections across all trials. Sensitivity for normal versus mildly abnormal varied between 61-79% across trials. CONCLUSIONS: With minimal training, crowdsourcing represents an accurate, rapid and cost-effective method of retinal image analysis which demonstrates good repeatability. Larger studies with more comprehensive participant training are needed to explore the utility of this compelling technique in large scale medical image analysis

Crowdsourcing as a screening tool to detect clinical features of glaucomatous optic neuropathy from digital photography.

AIM: Crowdsourcing is the process of simplifying and outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing in the classification of normal and glaucomatous discs from optic disc images. METHODS: Optic disc images (N = 127) with pre-determined disease status were selected by consensus agreement from grading experts from a large cohort study. After reading brief illustrative instructions, we requested that knowledge workers (KWs) from a crowdsourcing platform (Amazon MTurk) classified each image as normal or abnormal. Each image was classified 20 times by different KWs. Two study designs were examined to assess the effect of varying KW experience and both study designs were conducted twice for consistency. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC). RESULTS: Overall, 2,540 classifications were received in under 24 hours at minimal cost. The sensitivity ranged between 83-88% across both trials and study designs, however the specificity was poor, ranging between 35-43%. In trial 1, the highest AUC (95%CI) was 0.64(0.62-0.66) and in trial 2 it was 0.63(0.61-0.65). There were no significant differences between study design or trials conducted. CONCLUSIONS: Crowdsourcing represents a cost-effective method of image analysis which demonstrates good repeatability and a high sensitivity. Optimisation of variables such as reward schemes, mode of image presentation, expanded response options and incorporation of training modules should be examined to determine their effect on the accuracy and reliability of this technique in retinal image analysis

The accuracy and reliability of crowdsource annotations of digital retinal images

PURPOSE: Crowdsourcing is based on outsourcing computationally intensive tasks to numerous individuals in the online community who have no formal training. Our aim was to develop a novel online tool designed to facilitate large-scale annotation of digital retinal images, and to assess the accuracy of crowdsource grading using this tool, comparing it to expert classification.METHODS: We used 100 retinal fundus photograph images with predetermined disease criteria selected by two experts from a large cohort study. The Amazon Mechanical Turk Web platform was used to drive traffic to our site so anonymous workers could perform a classification and annotation task of the fundus photographs in our dataset after a short training exercise. Three groups were assessed: masters only, nonmasters only and nonmasters with compulsory training. We calculated the sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) plots for all classifications compared to expert grading, and used the Dice coefficient and consensus threshold to assess annotation accuracy.RESULTS: In total, we received 5389 annotations for 84 images (excluding 16 training images) in 2 weeks. A specificity and sensitivity of 71% (95% confidence interval [CI], 69%-74%) and 87% (95% CI, 86%-88%) was achieved for all classifications. The AUC in this study for all classifications combined was 0.93 (95% CI, 0.91-0.96). For image annotation, a maximal Dice coefficient (∼0.6) was achieved with a consensus threshold of 0.25.CONCLUSIONS: This study supports the hypothesis that annotation of abnormalities in retinal images by ophthalmologically naive individuals is comparable to expert annotation. The highest AUC and agreement with expert annotation was achieved in the nonmasters with compulsory training group.TRANSLATIONAL RELEVANCE: The use of crowdsourcing as a technique for retinal image analysis may be comparable to expert graders and has the potential to deliver timely, accurate, and cost-effective image analysis.</p

Associations with photoreceptor thickness measures in the UK Biobank.

Spectral-domain OCT (SD-OCT) provides high resolution images enabling identification of individual retinal layers. We included 32,923 participants aged 40-69 years old from UK Biobank. Questionnaires, physical examination, and eye examination including SD-OCT imaging were performed. SD OCT measured photoreceptor layer thickness includes photoreceptor layer thickness: inner nuclear layer-retinal pigment epithelium (INL-RPE) and the specific sublayers of the photoreceptor: inner nuclear layer-external limiting membrane (INL-ELM); external limiting membrane-inner segment outer segment (ELM-ISOS); and inner segment outer segment-retinal pigment epithelium (ISOS-RPE). In multivariate regression models, the total average INL-RPE was observed to be thinner in older aged, females, Black ethnicity, smokers, participants with higher systolic blood pressure, more negative refractive error, lower IOPcc and lower corneal hysteresis. The overall INL-ELM, ELM-ISOS and ISOS-RPE thickness was significantly associated with sex and race. Total average of INL-ELM thickness was additionally associated with age and refractive error, while ELM-ISOS was additionally associated with age, smoking status, SBP and refractive error; and ISOS-RPE was additionally associated with smoking status, IOPcc and corneal hysteresis. Hence, we found novel associations of ethnicity, smoking, systolic blood pressure, refraction, IOPcc and corneal hysteresis with photoreceptor thickness

Rhegmatogenous retinal detachment in Scotland: research design and methodology

<p>Abstract</p> <p>Background</p> <p>Rhegmatogenous retinal detachment (RRD) is a potentially blinding condition and a common cause of ocular morbidity. Establishing an accurate estimate of disease incidence and distribution is an important first step in assessing the healthcare burden related to this condition and in subsequent planning and provision of treatment strategies. The aim of this study is to obtain a first estimate incidence of RRD in Scotland, to estimate the incidence of familial RRD and to describe the known associations of RRD within the study population.</p> <p>Methods/Design</p> <p>We have established a national prospective observational study seeking to identify and recruit all incident cases of RRD in the Scottish population over a 2 year period. After fully informed consent, all participants will have a blood sample taken and a full medical history and clinical examination performed including visual acuity, refraction, slit-lamp examination, intra-ocular pressure measurement and detailed fundal examination. We describe the study design and protocol.</p> <p>Conclusion</p> <p>This study will provide the first estimate of the annual incidence of RRD in Scotland. The findings of this study will be important in estimating the burden of disease and in the planning of future health care policy related to this condition. This study will also establish a genetic resource for a genome wide association study to investigate if certain genetic variants predispose to RRD.</p

The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images

Purpose: Crowdsourcing is based on outsourcing computationally intensive tasks to numerous individuals in the online community who have no formal training. Our aim was to develop a novel online tool designed to facilitate large-scale annotation of digital retinal images, and to assess the accuracy of crowdsource grading using this tool, comparing it to expert classification. Methods: We used 100 retinal fundus photograph images with predetermined disease criteria selected by two experts from a large cohort study. The Amazon Mechanical Turk Web platform was used to drive traffic to our site so anonymous workers could perform a classification and annotation task of the fundus photographs in our dataset after a short training exercise. Three groups were assessed: masters only, nonmasters only and nonmasters with compulsory training. We calculated the sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) plots for all classifications compared to expert grading, and used the Dice coefficient and consensus threshold to assess annotation accuracy. Results: In total, we received 5389 annotations for 84 images (excluding 16 training images) in 2 weeks. A specificity and sensitivity of 71% (95% confidence interval [CI], 69%-74%) and 87% (95% CI, 86%-88%) was achieved for all classifications. The AUC in this study for all classifications combined was 0.93 (95% CI, 0.91-0.96). For image annotation, a maximal Dice coefficient (~0.6) was achieved with a consensus threshold of 0.25. Conclusions: This study supports the hypothesis that annotation of abnormalities in retinal images by ophthalmologically naive individuals is comparable to expert annotation. The highest AUC and agreement with expert annotation was achieved in the nonmasters with compulsory training group. Translational Relevance: The use of crowdsourcing as a technique for retinal image analysis may be comparable to expert graders and has the potential to deliver timely, accurate, and cost-effective image analysis

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies.

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost

Suitability of UK biobank retinal images for automatic analysis of morphometric properties of the vasculature

To assess the suitability of retinal images held in the UK Biobank--the largest retinal data repository in a prospective population-based cohort--for computer assisted vascular morphometry, generating measures that are commonly investigated as candidate biomarkers of systemic disease.Non-mydriatic fundus images from both eyes of 2,690 participants--people with a self-reported history of myocardial infarction (n=1,345) and a matched control group (n=1,345)--were analysed using VAMPIRE software. These images were drawn from those of 68,554 UK Biobank participants who underwent retinal imaging at recruitment. Four operators were trained in the use of the software to measure retinal vascular tortuosity and bifurcation geometry.Total operator time was approximately 360 hours (4 minutes per image). 2,252 (84%) of participants had at least one image of sufficient quality for the software to process, i.e. there was sufficient detection of retinal vessels in the image by the software to attempt the measurement of the target parameters. 1,604 (60%) of participants had an image of at least one eye that was adequately analysed by the software, i.e. the measurement protocol was successfully completed. Increasing age was associated with a reduced proportion of images that could be processed (p=0.0004) and analysed (p<0.0001). Cases exhibited more acute arteriolar branching angles (p=0.02) as well as lower arteriolar and venular tortuosity (p<0.0001).A proportion of the retinal images in UK Biobank are of insufficient quality for automated analysis. However, the large size of the UK Biobank means that tens of thousands of images are available and suitable for computational analysis. Parametric information measured from the retinas of participants with suspected cardiovascular disease was significantly different to that measured from a matched control group

Association between polygenic risk score and risk of myopia

YesImportance: Myopia is a leading cause of untreatable visual impairment and is increasing in prevalence worldwide. Interventions for slowing childhood myopia progression have shown success in randomized clinical trials; hence, there is a need to identify which children would benefit most from treatment intervention. Objectives: To examine whether genetic information alone can identify children at risk of myopia development and whether including a child’s genetic predisposition to educational attainment is associated with improved genetic prediction of the risk of myopia. Design, Setting, and Participants: Meta-analysis of 3 genome-wide association studies (GWAS) including a total of 711 984 individuals. These were a published GWAS for educational attainment and 2 GWAS for refractive error in the UK Biobank, which is a multisite cohort study that recruited participants between January 2006 and October 2010. A polygenic risk score was applied in a population-based validation sample examined between September 1998 and September 2000 (Avon Longitudinal Study of Parents and Children [ALSPAC] mothers). Data analysis was performed from February 2018 to May 2019. Main Outcomes and Measures: The primary outcome was the area under the receiver operating characteristic curve (AUROC) in analyses for predicting myopia, using noncycloplegic autorefraction measurements for myopia severity levels of less than or equal to −0.75 diopter (D) (any), less than or equal to -3.00 D (moderate), or less than or equal to −5.00 D (high). The predictor variable was a polygenic risk score (PRS) derived from genome-wide association study data for refractive error (n = 95 619), age of onset of spectacle wear (n = 287 448), and educational attainment (n = 328 917). Results: A total of 383 067 adults aged 40 to 69 years from the UK Biobank were included in the new GWAS analyses. The PRS was evaluated in 1516 adults aged 24 to 51 years from the ALSPAC mothers cohort. The PRS had an AUROC of 0.67 (95% CI, 0.65-0.70) for myopia, 0.75 (95% CI, 0.70-0.79) for moderate myopia, and 0.73 (95% CI, 0.66-0.80) for high myopia. Inclusion in the PRS of information associated with genetic predisposition to educational attainment marginally improved the AUROC for myopia (AUROC, 0.674 vs 0.668; P = .02), but not those for moderate and high myopia. Individuals with a PRS in the top 10% were at 6.1-fold higher risk (95% CI, 3.4–10.9) of high myopia. Conclusions and Relevance: A personalized medicine approach may be feasible for detecting very young children at risk of myopia. However, accuracy must improve further to merit uptake in clinical practice; currently, cycloplegic autorefraction remains a better indicator of myopia risk (AUROC, 0.87).PhD studentship grant from the College of Optometrists (Drs Guggenheim and Williams; supporting Mr Mojarrad) entitled Genetic prediction of individuals at-risk for myopia development) and National Institute for Health Research (NIHR) Senior Research Fellowship award SRF-2015-08-005 (Dr Williams). The UK Medical Research Council and Wellcome grant 102215/2/13/2 and the University of Bristol provide core support for the Avon Longitudinal Study of Parents and Children (ALSPAC). A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). This research was conducted using the UK Biobank Resource (application 17351). The UK Biobank was established by the Wellcome Trust, the UK Medical Research Council, the Department for Health (London, England), the Scottish government (Edinburgh, Scotland), and the Northwest Regional Development Agency (Warrington, England). It also received funding from the Welsh Assembly Government (Cardiff, Wales), the British Heart Foundation, and Diabetes UK