A study of whether automated Diabetic Retinopathy Image Assessment could replace manual grading steps in the English National Screening Programme.
OBJECTIVE
Diabetic retinopathy screening in England involves labour intensive manual grading of digital retinal images. We present the plan for an observational retrospective study of whether automated systems could replace one or more steps of human grading.


METHODS
Patients aged 12 or older who attended the Diabetes Eye Screening programme, Homerton University Hospital (London) between 1 June 2012 and 4 November 2013 had macular and disc-centred retinal images taken. All screening episodes were manually graded and will additionally be graded by three automated systems. Each system will process all screening episodes, and screening performance (sensitivity, false positive rate, likelihood ratios) and diagnostic accuracy (95% confidence intervals of screening performance measures) will be quantified. A sub-set of gradings will be validated by an approved Reading Centre. Additional analyses will explore the effect of altering thresholds for disease detection within each automated system on screening performance.


RESULTS
2,782/20,258 diabetes patients were referred to ophthalmologists for further examination. Prevalence of maculopathy (M1), pre-proliferative retinopathy (R2), and proliferative retinopathy (R3) were 7.9%, 3.1% and 1.2%, respectively; 4749 (23%) patients were diagnosed with background retinopathy (R1); 1.5% were considered ungradable by human graders.


CONCLUSIONS
Retinopathy prevalence was similar to other English diabetic screening programmes, so findings should be generalizable. The study population size will allow the detection of differences in screening performance between the human and automated grading systems as small as 2%. The project will compare performance and economic costs of manual versus automated systems.
Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, United Kingdom vkapetan@sgul.ac.uk.
Venediktos V
VV
Kapetanakis
Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, United Kingdom.
Alicja R
AR
Rudnicka
Centre for Vision Research, University of Sydney, NSW 2006, Australia.
Gerald
G
Liew
Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, United Kingdom.
Christopher G
CG
Owen
Moorfields BRC, Moorfields Eye Hospital, London, EC1V 2PD, United Kingdom.
Aaron
A
Lee
Moorfields BRC, Moorfields Eye Hospital, London, EC1V 2PD, United Kingdom.
Vern
V
Louw
Homerton University Hospital, Homerton Row, E9 6SR.
Louis
L
Bolter
Homerton University Hospital, Homerton Row, E9 6SR.
John
J
Anderson
Moorfields BRC, Moorfields Eye Hospital, London, EC1V 2PD, United Kingdom.
Catherine
C
Egan
Department of Social Policy, LSE Health, London School of Economics and Political Science, London, WC2A 2AE, United Kingdom.
Sebastian
S
Salas-Vega
Department of Social Policy, LSE Health, London School of Economics and Political Science, London, WC2A 2AE, United Kingdom.
Caroline
C
Rudisill
CHIME, Institute of Health Informatics, University College London, London, NW1 2HE, United Kingdom.
Paul
P
Taylor
Moorfields BRC, Moorfields Eye Hospital, London, EC1V 2PD, United Kingdom.
Adnan
A
Tufail
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000592:standards*; D004739:England; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods* / Q000592:standards*; D008875:Middle Aged; D009885:Ophthalmology / Q000379:methods / Q000592:standards; D010363:Pattern Recognition, Automated; D012189:Retrospective Studies; D055815:Young Adult