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

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

doi:10.1155/2016/4176547

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.

Highlights

Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service and can be time consuming to identify for human graders [1]
The images from Kenya and Norway originate from population screening, while those from Botswana and Mongolia originate from DR screening
The sensitivities, defined as the proportion of patients classified as having microaneurysms by human graders that were correctly identified by the automated system, were as follows: Kenya 92.8%, Botswana 90.1%, Norway 93.5%, Mongolia

Summary

Introduction

Patients without diabetic retinopathy (DR) represent a large proportion of the caseload seen by the DR screening service and can be time consuming to identify for human graders [1]. Microaneurysms (MAs) are the first visible sign of DR and their detection has been widely described in the literature as an important factor in identifying the onset of DR [2, 3]. Reliable recognition of the absence of microaneurysms initially on FFA [4] and subsequently in digital fundus images [5] (DFIs) has been a prime focus of automated DR screening research for many years. DR detection is partly undertaken through contrast of the MA with the background field of the DFIs. The appearance of the background field in DFIs is highly variable and is influenced by many factors including the degree of pigmentation in the retinal pigment epithelium and choroid, the size of the pupil, unevenness in illumination, poor transparency of the ocular media in corneal disease and cataract, and the camera type and its settings [6]. Variability in the background field is further complicated by well-reported racial variance, for example, in the calibre of retinal blood vessels [7,8,9]

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