Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes
Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes
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DOI：
10.2337/dc07-1312
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发表时间：
2008-02-01
期刊：
影响因子：
16.2
通讯作者：
van Ginneken, Bram
中科院分区：
文献类型：
--
作者：
Abramoff, Michael D.;Niemeijer, Meindert;van Ginneken, Bram
目的-为了评估一个系统的性能自动检测糖尿病视网膜病变的数字视网膜照片，建立从出版的算法，在一个大的，有代表性的，筛选population.Research设计和方法-我们进行了一项回顾性分析，10,000连续病人访问，特别是考试来自EyeCheck糖尿病视网膜病变筛查项目的5，692名独特患者的四张视网膜照片（两张左，两张右），在10个中心使用三种类型的相机成像。入选标准包括既往无糖尿病视网膜病变诊断，既往无眼科医生散瞳检查，双眼拍照。三位视网膜专家中的一位将每次检查评价为不可接受的质量，没有可诊断的视网膜病变或可诊断的视网膜病变。然后，我们选择具有足够图像质量的检查，并确定是否存在可诊断的视网膜病变。结果测量包括受试者工作特征曲线下的面积（漏诊一例所需的数量[NNM]）和假阴性的类型。结果-受试者工作特征曲线下的总面积为0.84，NNM为80，灵敏度为0.84，特异性为0.64。在这一点上，10，000次检查中有7，689次具有足够的图像质量，7，689次检查中有4，648次（60%）为真阴性，7，689次检查中有59次（0.8%）为假阴性，7，689次检查中有319次（4%）为真阳性，7，689次检查中有2，581次（33%）为假阳性。27%的假阴性包含大血管和/或新生血管。使用已发表的算法还不能。推荐用于临床实践。然而，性能是这样的评价石油验证，公开可用的数据集应追求。如果算法可以改进，这样的系统可能在未来导致糖尿病患者的失明和视力丧失的改进预防。
OBJECTIVE - To evaluate the performance of a system for automated detection of diabetic retinopathy in digital retinal photographs, built from published algorithms, in a large, representative, screening population.RESEARCH DESIGN AND METHODS - We conducted a retrospective analysis of 10,000 consecutive Patient visits, specifically exams (four retinal photographs, two left and two right) from 5,692 unique patients from the EyeCheck diabetic retinopathy screening project imaged with three types of cameras at 10 centers. Inclusion criteria included no previous diagnosis of diabetic retinopathy, no previous visit to ophthalmologist for dilated eye exam, and both eyes photographed. One Of three retinal specialists evaluated each exam as unacceptable quality, no referable retinopathy, or referable retinopathy. We then selected exams with sufficient image quality and determined presence or absence of referable retinopathy. Outcome measures included area tinder the receiver operating characteristic curve (number needed to miss one case [NNM]) and type of false negative.RESULTS - Total area under the receiver operating characteristic curve was 0.84, and NNM was 80 at a sensitivity of 0.84 and a specificity of 0.64. At this point, 7,689 of 10,000 exams had sufficient image quality, 4,648 of 7,689 (60%) were true negatives, 59 of 7,689 (0.8%) were false negatives, 319 of 7,689 (4%) were true positives, and 2,581 of 7,689 (33%) were false Positives. Twenty-seven percent of false negatives contained large hemorrhages and/or neovascularizations.CONCLUSIONS - Automated detection. of diabetic retinopathy Using published algorithms cannot yet. be recommended for clinical practice. However, performance is such that evaluation Oil validated, publicly available datasets should be pursued. if algorithms can be improved, such a System May in the future lead to improved prevention of blindness and vision loss in patients With diabetes.