论文标题
使用深度学习预测患糖尿病性视网膜病变的风险
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
论文作者
论文摘要
糖尿病性视网膜病（DR）筛查对预防失明有用，但随着糖尿病患者数量的增加，面临缩放挑战。开发DR的风险分层可能有助于优化筛查间隔以降低成本，同时改善与视力相关的结果。我们创建并验证了深度学习系统（DLS）的两个版本，以预测接受DR筛查的糖尿病患者的轻度或伤人（“轻度+”）DR的发展。这两个版本使用三场或单个彩色眼底照片（CFP）作为输入。训练集源自575,431只眼睛，其中28,899只已知2年的结果，其余的是通过多任务学习来增强训练过程。对内部验证集（集合A; 7,976眼； 3,678张有已知结果）和外部验证集（集合B; 4,762眼； 2,345; 2,345; 2,345;带有已知结果）进行了验证。为了预测DR的两年开发，在验证集A上，3型场DLS在接收器操作特征曲线（AUC）下的面积为0.79（95％CI，0.78-0.81）。即使调整了可用的风险因素，DLS也是预后的（p <0.001）。当添加到风险因素中时，3局DLS从0.72（95％CI，0.68-0.76）提高到0.81（95％CI，0.77-0.84）的验证集A集合A，1-field DLS将AUC提高了AUC，AUC从0.62（95％CI，0.58-0.66）提高了AUC，在验证集合中。本研究中的DLSS确定了CFPS DR开发的预后信息。与可用的风险因素相比，此信息独立于和更有信息。
Diabetic retinopathy (DR) screening is instrumental in preventing blindness, but faces a scaling challenge as the number of diabetic patients rises. Risk stratification for the development of DR may help optimize screening intervals to reduce costs while improving vision-related outcomes. We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic patients undergoing DR screening. The two versions used either three-fields or a single field of color fundus photographs (CFPs) as input. The training set was derived from 575,431 eyes, of which 28,899 had known 2-year outcome, and the remaining were used to augment the training process via multi-task learning. Validation was performed on both an internal validation set (set A; 7,976 eyes; 3,678 with known outcome) and an external validation set (set B; 4,762 eyes; 2,345 with known outcome). For predicting 2-year development of DR, the 3-field DLS had an area under the receiver operating characteristic curve (AUC) of 0.79 (95%CI, 0.78-0.81) on validation set A. On validation set B (which contained only a single field), the 1-field DLS's AUC was 0.70 (95%CI, 0.67-0.74). The DLS was prognostic even after adjusting for available risk factors (p<0.001). When added to the risk factors, the 3-field DLS improved the AUC from 0.72 (95%CI, 0.68-0.76) to 0.81 (95%CI, 0.77-0.84) in validation set A, and the 1-field DLS improved the AUC from 0.62 (95%CI, 0.58-0.66) to 0.71 (95%CI, 0.68-0.75) in validation set B. The DLSs in this study identified prognostic information for DR development from CFPs. This information is independent of and more informative than the available risk factors.
