Hansen Morten B, Abràmoff Michael D, Folk James C, Mathenge Wanjiku, Bastawrous Andrew, Peto Tunde
NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophtalmology, London, United Kingdom; Research Unit of Ophthalmology, University of Southern Denmark, Odense, Denmark.
Department of Ophthalmology and Visual Sciences, University of Iowa Hospital and Clinics, Iowa City, IA, 52242, United States of America.
PLoS One. 2015 Oct 1;10(10):e0139148. doi: 10.1371/journal.pone.0139148. eCollection 2015.
Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields Reading Centre on the population of Nakuru Study from Kenya.
Retinal images were taken from participants of the Nakuru Eye Disease Study in Kenya in 2007/08 (n = 4,381 participants [NW6 Topcon Digital Retinal Camera]).
First, human grading was performed for the presence or absence of DR, and for those with DR this was sub-divided in to referable or non-referable DR. The automated IDP software was deployed to identify those with DR and also to categorize the severity of DR.
The primary outcomes were sensitivity, specificity, and positive and negative predictive value of IDP versus the human grader as reference standard.
Altogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3% (95% CI, 2.7-3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0% (95% CI, 88.0-93.4%). The IDP ability to detect DED gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment.
In this epidemiological sample, the IDP's grading was comparable to that of human graders'. It therefore might be feasible to consider inclusion into usual epidemiological grading.
数字视网膜成像技术是一种成熟的糖尿病视网膜病变（DR）筛查方法。目前已经确定，全球约1%的盲人或视力受损者是由DR所致。然而，糖尿病和DR患病率的不断上升，给视网膜图像分级专家带来了日益增加的工作量。安全可靠的视网膜图像自动分析可能会为全球的筛查服务提供支持。本研究旨在比较爱荷华检测程序（IDP）检测糖尿病眼病（DED）的能力与在莫菲尔德阅读中心对肯尼亚纳库鲁研究人群进行的人工分级。
2007/08年从肯尼亚纳库鲁眼病研究的参与者中获取视网膜图像（n = 4381名参与者[NW6 Topcon数字视网膜相机]）。
首先，对是否存在DR进行人工分级，对于患有DR的患者，将其分为可转诊或不可转诊的DR。部署自动IDP软件以识别患有DR的患者，并对DR的严重程度进行分类。
主要结果是IDP相对于作为参考标准的人工分级者的敏感性、特异性、阳性和阴性预测值。
总共纳入3460名参与者。113人患有DED，患病率为3.3%（95%可信区间，2.7 - 3.9%）。IDP检测DED的敏感性与人工分级相比为91.0%（95%可信区间，88.0 - 93.4%）。IDP检测DED的能力的曲线下面积为0.878（95%可信区间0.850 - 0.905）。其阴性预测值为98%。IDP在任何患者中均未漏诊有视力威胁的视网膜病变，且所有假阴性病例均不符合治疗标准。
在这个流行病学样本中，IDP的分级与人工分级者的分级相当。因此，考虑将其纳入常规流行病学分级可能是可行的。
