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Two automated retinal image analysis systems achieved acceptable sensitivity and false positive rates for referable retinopathy, compared to human graders, and were cost saving.

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Adnan Tufail, Venediktos V Kapetanakis, Sebastian Salas-Vega, Catherine Egan, Caroline Rudisill, Christopher G Owen, Aaron Lee, Vern Louw, John Anderson, Gerald Liew, Louis Bolter, Clare Bailey, SriniVas Sadda, Paul Taylor & Alicja R Rudnicka.

Adnan Tufail 1,*, Venediktos V Kapetanakis 2, Sebastian Salas-Vega 3, Catherine Egan 1, Caroline Rudisill 3, Christopher G Owen 2, Aaron Lee 1, Vern Louw 1, John Anderson 4, Gerald Liew 1, Louis Bolter 4, Clare Bailey 5, SriniVas Sadda 6, Paul Taylor 7, Alicja R Rudnicka 2

1 National Institute for Health Research Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
2 Population Health Research Institute, St George’s, University of London, London, UK
3 Department of Social Policy, LSE Health, London School of Economics and Political Science, London, UK
4 Homerton University Hospital Foundation Trust, London, UK
5 Bristol Eye Hospital, Bristol, UK
6 Doheny Eye Institute, Los Angeles, CA, USA
7 Centre for Health Informatics & Multiprofessional Education (CHIME), Institute of Health Informatics, University College London, London, UK
* Corresponding author Email:

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