Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images
Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images
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DOI：
10.1111/aos.13528
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发表时间：
2018-03-01
影响因子：
3.4
通讯作者：
Sadda, SriniVas R.
中科院分区：
文献类型：
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作者：
Wang, Kang;Jayadev, Chaitra;Sadda, SriniVas R.
PurposeWe检查了一种自动算法的灵敏度和特异性，用于检测Optos超宽场（UWF）伪彩色images.MethodsPatients糖尿病患者招募UWF成像的糖尿病视网膜病变（DR）。共入组383例受试者（754只眼）。根据5级国际临床糖尿病视网膜病变（ICDR）严重程度量表，非增殖性DR分级为中度或更高，被视为转诊的理由。该软件使用先前训练的分类器自动检测DR病变，并将测试集中的每张图像分类为经验证的或未经验证的。敏感性，结果自动算法的敏感性分别为91.7%/90.3（95% CI 90.1-93.9/80.4-89.4），特异性为50.0%/53.6%（95% CI 31.7-72.8/36.5-71.4）分别用于检测患者/眼睛水平的视网膜病变; AUROC为0.873/0.851（95%CI为0.819-0.922/0.804-0.894）。影像被归类为具有高度敏感性和中度特异性的经病理证实的DR。UWF图像的自动分析可能在DR筛查项目中具有价值，并且可以实现更完整和准确的疾病分期。
PurposeWe examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images.MethodsPatients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed.ResultsThe automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1-93.9/80.4-89.4) with a 50.0%/53.6% specificity (95% CI 31.7-72.8/36.5-71.4) for detecting referral-warranted retinopathy at the patient/eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819-0.922/0.804-0.894).ConclusionDiabetic retinopathy (DR) lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging.