TY -非盟的布雷迪,Christopher J AU - Villanti,安德里亚·C AU -皮尔森Jennifer L AU -基什内尔Thomas R AU -古普塔,Omesh P AU - Shah是P PY - 2014 DA - 2014/10/30 TI -快速分级糖尿病性视网膜病变的眼底照片使用众包乔- J地中海互联网Res SP - e233六世16 - 10 KW -糖尿病视网膜病变KW -远程医疗KW -眼底摄影KW -众包KW -亚马逊土耳其机器人AB -背景:糖尿病视网膜病变筛查既有效又经济，但筛查依从性仍不理想。随着筛查的改进，处理筛查数据的新方法可能有助于减少人力资源的需求。众包已经在许多情况下用于利用分布式人类智能来完成小任务，包括图像分类。目的:我们的目标是开发并验证一种新的眼底照片分级方法。方法:为亚马逊土耳其机器人众包平台开发了一个用于眼底照片分类的界面。我们发布了19张专家分级的图片供土耳其人分级，每张照片重复10次，作为最初的概念验证(第一阶段)。土耳其人每张图片获得0.10美元的报酬。在第二阶段，来自四个分级类别的每个原型图像收到500个独特的土耳其人解释。然后使用50次1-50个土耳其人的抽签来估计从随机抽取的人群规模增加的样本中获得的准确性方差，以确定产生有效结果所需的最小土耳其人数量。在第三阶段，对界面进行了修改，试图提高Turker分级。 Results: Across 230 grading instances in the normal versus abnormal arm of Phase I, 187 images (81.3%) were correctly classified by Turkers. Average time to grade each image was 25 seconds, including time to review training images. With the addition of grading categories, time to grade each image increased and percentage of images graded correctly decreased. In Phase II, area under the curve (AUC) of the receiver-operator characteristic (ROC) indicated that sensitivity and specificity were maximized after 7 graders for ratings of normal versus abnormal (AUC=0.98) but was significantly reduced (AUC=0.63) when Turkers were asked to specify the level of severity. With improvements to the interface in Phase III, correctly classified images by the mean Turker grade in four-category grading increased to a maximum of 52.6% (10/19 images) from 26.3% (5/19 images). Throughout all trials, 100% sensitivity for normal versus abnormal was maintained. Conclusions: With minimal training, the Amazon Mechanical Turk workforce can rapidly and correctly categorize fundus photos of diabetic patients as normal or abnormal, though further refinement of the methodology is needed to improve Turker ratings of the degree of retinopathy. Images were interpreted for a total cost of US $1.10 per eye. Crowdsourcing may offer a novel and inexpensive means to reduce the skilled grader burden and increase screening for diabetic retinopathy. SN - 1438-8871 UR - //www.mybigtv.com/2014/10/e233/ UR - https://doi.org/10.2196/jmir.3807 UR - http://www.ncbi.nlm.nih.gov/pubmed/25356929 DO - 10.2196/jmir.3807 ID - info:doi/10.2196/jmir.3807 ER -