Saleh George Michael, Wawrzynski James, Caputo Silvestro, Peto Tunde, Al Turk Lutfiah Ismail, Wang Su, Hu Yin, Da Cruz Lyndon, Smith Phil, Tang Hongying Lilian
Moorfields Eye Hospital NHS Foundation Trust, London, UK; Department of Computing, Faculty of Engineering, University of Surrey, Guildford, UK; National Institute for Health Research Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
Barking, Havering and Redbridge University Hospitals Trust, London, UK.
J Ophthalmol. 2016;2016:4176547. doi: 10.1155/2016/4176547. Epub 2016 Dec 15.
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.
没有糖尿病视网膜病变（DR）的患者占DR筛查服务所处理病例的很大比例，因此在数字眼底图像（DFI）中可靠识别无DR情况是自动DR筛查研究的主要重点。我们研究了一种新型自动DR检测算法用于评估视网膜DFI有无DR的情况。开展了一项基于图像的回顾性、盲法和对照研究。通过自动系统和人工分级对来自六个不同国家的17850例患者的DFI进行了DR评估。对不同国家/种族群体的DFI的系统性能进行了比较。自动系统检测DR的敏感度分别为：肯尼亚92.8%、博茨瓦纳90.1%、挪威93.5%、蒙古91.3%、中国91.9%、英国90.1%。特异度分别为：肯尼亚82.7%、博茨瓦纳83.2%、挪威81.3%、蒙古82.5%、中国83.0%、英国79%。参与研究的六个不同国家所计算出的敏感度和特异度几乎没有差异。这些数据表明自动DR检测平台具有可扩展性，能够在广泛的种族范围内快速识别无DR的患者。
