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

George Michael Saleh, James Wawrzynski, Silvestro Caputo, Tunde Peto, Lutfiah Ismail Al Turk, Su Wang, Yin Hu, Lyndon Da Cruz, Phil Smith, Hongying Lilian Tang

Research output: Contribution to journalArticle

2 Citations (Scopus)
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Abstract

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.

Original languageEnglish
Article number4176547
Number of pages5
JournalJournal of Ophthalmology
Volume2016
DOIs
Publication statusPublished - 01 Aug 2016

Keywords

  • Journal Article

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