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Journal of Ophthalmology
Volume 2016 (2016), Article ID 4176547, 5 pages
Research Article

An Automated Detection System for Microaneurysms That Is Effective across Different Racial Groups

1Moorfields Eye Hospital NHS Foundation Trust, London, UK
2Department of Computing, Faculty of Engineering, University of Surrey, Guildford, UK
3National Institute for Health Research Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
4Barking, Havering and Redbridge University Hospitals Trust, London, UK
5Statistics Department, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
6Institute of Ophthalmology, UCL, London, UK

Received 22 March 2016; Revised 28 June 2016; Accepted 10 July 2016

Academic Editor: Neil Lagali

Copyright © 2016 George Michael Saleh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service so reliable recognition of the absence of DR in digital fundus images (DFIs) is a prime focus of automated DR screening research. We investigate the use of a novel automated DR detection algorithm to assess retinal DFIs for absence of DR. A retrospective, masked, and controlled image-based study was undertaken. 17,850 DFIs of patients from six different countries were assessed for DR by the automated system and by human graders. The system’s performance was compared across DFIs from the different countries/racial groups. The sensitivities for detection of DR by the automated system were Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia 91.3%, China 91.9%, and UK 90.1%. The specificities were Kenya 82.7%, Botswana 83.2%, Norway 81.3%, Mongolia 82.5%, China 83.0%, and UK 79%. There was little variability in the calculated sensitivities and specificities across the six different countries involved in the study. These data suggest the possible scalability of an automated DR detection platform that enables rapid identification of patients without DR across a wide range of races.