UK Biobank retinal imaging grading: methodology, baseline characteristics and findings for common ocular diseases


  • Fight For Sight. Time to focus. 2020. https://www.fightforsight.org.uk/media/3302/time-to-focus-report.pdf.

  • GBD 2019 Blindness and Vision Impairment Collaborators & Vision Loss Expert Group of the Global Burden of Disease Study Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9:e144–e160.

    Article 

    Google Scholar 

  • Yau JWY, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556–64.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, et al. IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40–50.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Demmin DL, Silverstein SM. Visual impairment and mental health: unmet needs and treatment options. Clin Ophthalmol (Auckl, N. Z.). 2020;14:4229–51.

    Article 

    Google Scholar 

  • Kortuem K, Fasler K, Charnley A, Khambati H, Fasolo S, Katz M, et al. Implementation of medical retina virtual clinics in a tertiary eye care referral centre. Br J Ophthalmol. 2018;102:1391–5.

    Article 
    PubMed 

    Google Scholar 

  • Wagner SK, Fu DJ, Faes L, Liu X, Huemer J, Khalid H, et al. Insights into systemic disease through retinal imaging-based oculomics. Transl Vis Sci Technol. 2020;9:6.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chua SYL, Thomas D, Allen N, Lotery A, Desai P, Patel P, et al. Cohort profile: design and methods in the eye and vision consortium of UK Biobank. BMJ open. 2019;9:e025077.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • UK Biobank. UK Biobank research ethics approval. 2021. https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics.

  • Kuan V, Denaxas S, Gonzalez-Izquierdo A, Direk K, Bhatti O, Husain S, et al. A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digital Health. 2019;1:e63–e77.

    Article 
    PubMed 

    Google Scholar 

  • Foster PJ, Buhrmann R, Quigley HA, Johnson GJ. The definition and classification of glaucoma in prevalence surveys. Br J Ophthalmol. 2002;86:238–42.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Landau WM. The targets r package: a dynamic make-like function-oriented pipeline toolkit for reproducibility and high-performance computing. J Open Source Softw. 2021;6:2959.

  • Landau WM. Tarchetypes: archetypes for targets. 2021. https://docs.ropensci.org/tarchetypes/.

  • Blischak JD, Carbonetto P, Stephens M. Creating and sharing reproducible research code the workflowr way. F1000Res. 2019;8:1749.

  • Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R. et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.

    Article 

    Google Scholar 

  • Warwick A. Ukbwranglr: functions to load and wrangle UK Biobank data. 2022. https://rmgpanw.github.io/ukbwranglr/.

  • Warwick A. Codemapper: functions for mapping between clinical coding systems. 2022. https://rmgpanw.github.io/codemapper/.

  • Patil I. Visualizations with statistical details: the ’ggstatsplot’ approach. J Open Source Softw. 2021;6:3167.

  • Xie Y. Knitr: a general-purpose package for dynamic report generation in r. 2022. https://yihui.org/knitr/.

  • Sjoberg DD, Whiting K, Curry M, Lavery JA, Larmarange J. Reproducible summary tables with the gtsummary package. R J. 2021;13:570–80.

  • Gohel D. Flextable: functions for tabular reporting. 2022. https://CRAN.R-project.org/package=flextable.

  • Desai P, Minassian DC, Reidy A, Allen N, Sudlow C. Number of incident cases of the main eye diseases of ageing in the UK Biobank cohort, projected over a 25-year period from time of recruitment. Br J Ophthalmol. 2018;102:1533–7.

    Article 
    PubMed 

    Google Scholar 

  • Wong WL, Su X, Li X, Cheung CMG, Klein R, Cheng C-Y, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health. 2014;2:e106–116.

    Article 
    PubMed 

    Google Scholar 

  • Klein R, Meuer SM, Myers CE, Buitendijk GHS, Rochtchina E, Choudhury F, et al. Harmonizing the classification of age-related macular degeneration in the three-continent AMD consortium. Ophthalmic Epidemiol. 2014;21:14–23.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Brandl C, Zimmermann ME, Günther F, Barth T, Olden M, Schelter SC, et al. On the impact of different approaches to classify age-related macular degeneration: results from the German AugUR study. Sci Rep. 2018;8:8675.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ferris FL, Wilkinson CP, Bird A, Chakravarthy U, Chew E, Csaky K, et al. Clinical classification of age-related macular degeneration. Ophthalmology. 2013;120:844–51.

    Article 
    PubMed 

    Google Scholar 

  • Klein BE, Klein R, Sponsel WE, Franke T, Cantor LB, Martone J, et al. Prevalence of glaucoma. The Beaver Dam Eye Study. Ophthalmology. 1992;99:1499–504.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Silvestri G, Williams MA, McAuley C, Oakes K, Sillery E, Henderson DC, et al. Drusen prevalence and pigmentary changes in Caucasians aged 18-54 years. Eye (Lond, Engl). 2012;26:1357–62.

    Article 
    CAS 

    Google Scholar 

  • Schachat AP, Hyman L, Leske MC, Connell AM, Wu SY. Features of age-related macular degeneration in a black population. The Barbados Eye Study Group. Arch Ophthalmol (Chic, Ill 1960). 1995;113:728–35.

    Article 
    CAS 

    Google Scholar 

  • Bressler NM, Bressler SB, West SK, Fine SL, Taylor HR. The grading and prevalence of macular degeneration in Chesapeake Bay watermen. Arch Ophthalmol (Chic, Ill: 1960). 1989;107:847–52.

    Article 
    CAS 

    Google Scholar 

  • Munch IC, Sander B, Kessel L, Hougaard JL, Taarnhøj NCBB, Sørensen TIA, et al. Heredity of small hard drusen in twins aged 20-46 years. Investigative Ophthalmol Vis Sci. 2007;48:833–8.

    Article 

    Google Scholar 

  • Sandberg MA, Tolentino MJ, Miller S, Berson EL, Gaudio AR. Hyperopia and neovascularization in age-related macular degeneration. Ophthalmology. 1993;100:1009–13.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ikram MK, van Leeuwen R, Vingerling JR, Hofman A, de Jong PTVM. Relationship between refraction and prevalent as well as incident age-related maculopathy: the Rotterdam Study. Investigative Ophthalmol Vis Sci. 2003;44:3778–82.

    Article 

    Google Scholar 

  • Lavanya R, Kawasaki R, Tay WT, Cheung GCM, Mitchell P, Saw S-M, et al. Hyperopic refractive error and shorter axial length are associated with age-related macular degeneration: the Singapore Malay Eye Study. Investigative Ophthalmol Vis Sci. 2010;51:6247–52.

    Article 

    Google Scholar 

  • Jonas JB, Nangia V, Kulkarni M, Gupta R, Khare A. Associations of early age-related macular degeneration with ocular and general parameters. The Central India Eyes and Medical Study. Acta Ophthalmologica. 2012;90:e185–191.

    Article 
    PubMed 

    Google Scholar 

  • Li Y, Wang J, Zhong X, Tian Z, Wu P, Zhao W, et al. Refractive error and risk of early or late age-related macular degeneration: a systematic review and meta-analysis. PLoS One. 2014;9:e90897.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mitchell P, Smith W, Attebo K, Healey PR. Prevalence of open-angle glaucoma in Australia. The Blue Mountains Eye Study. Ophthalmology. 1996;103:1661–9.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Dielemans I, Vingerling JR, Wolfs RC, Hofman A, Grobbee DE, de Jong PT. The prevalence of primary open-angle glaucoma in a population-based study in The Netherlands. The Rotterdam Study. Ophthalmology. 1994;101:1851–5.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Coffey M, Reidy A, Wormald R, Xian WX, Wright L, Courtney P. Prevalence of glaucoma in the west of Ireland. Br J Ophthalmol. 1993;77:17–21.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Klein R, Klein BE, Moss SE, Wang Q. Hypertension and retinopathy, arteriolar narrowing, and arteriovenous nicking in a population. Arch Ophthalmol (Chic, Ill: 1960). 1994;112:92–98.

    Article 
    CAS 

    Google Scholar 

  • Frank RN. Diabetic retinopathy. N Engl J Med. 2004;350:48–58.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Bertelsen G, Peto T, Lindekleiv H, Schirmer H, Solbu MD, Toft I, et al. Tromsø eye study: prevalence and risk factors of diabetic retinopathy. Acta Ophthalmologica. 2013;91:716–21.

    Article 
    PubMed 

    Google Scholar 

  • Gunnlaugsdottir E, Halldorsdottir S, Klein R, Eiriksdottir G, Klein BE, Benediktsson R, et al. Retinopathy in old persons with and without diabetes mellitus: the Age, Gene/Environment Susceptibility–Reykjavik Study (AGES-R). Diabetologia. 2012;55:671–80.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lamparter J, Raum P, Pfeiffer N, Peto T, Höhn R, Elflein H, et al. Prevalence and associations of diabetic retinopathy in a large cohort of prediabetic subjects: the Gutenberg Health Study. J Diabetes Complications. 2014;28:482–7.

    Article 
    PubMed 

    Google Scholar 

  • Hubbard LD, Brothers RJ, King WN, Clegg LX, Klein R, Cooper LS, et al. Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. Ophthalmology. 1999;106:2269–80.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Vujosevic S, Benetti E, Massignan F, Pilotto E, Varano M, Cavarzeran F, et al. Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields. Am J Ophthalmol. 2009;148:111–8.

    Article 
    PubMed 

    Google Scholar 

  • Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2:158–64.

    Article 
    PubMed 

    Google Scholar 

  • Rim TH, Lee G, Kim Y, Tham Y-C, Lee CJ, Baik SJ, et al. Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms. Lancet Digital Health. 2020;2:e526–e536.

    Article 
    PubMed 

    Google Scholar 

  • Krause J, Gulshan V, Rahimy E, Karth P, Widner K, Corrado GS, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology. 2018;125:1264–72.

    Article 
    PubMed 

    Google Scholar 



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