Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity
Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity
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DOI：
10.1136/bjophthalmol-2018-313156
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发表时间：
2019-05-01
影响因子：
4.1
通讯作者：
Chiang, Michael F.
中科院分区：
文献类型：
--
作者：
Redd, Travis K.;Campbell, John Peter;Chiang, Michael F.
先前的工作已经证明了深度学习视网膜图像分析系统在诊断早产儿视网膜病变（ROP）中的准确性近乎完美。在这里，我们评估的筛选潜力，这个评分系统，通过确定其检测ROP diagnosis.Methods的所有组件的能力进行临床检查和眼底照相在七个参与中心。训练深度学习系统以检测plus疾病，在1-9量表上生成视网膜血管异常的定量评估（i-ROP plus评分）。使用结合临床和基于图像的诊断的共识参考标准诊断建立总体ROP疾病类别。然后，专家们根据总体ROP严重程度对第二组100张后验图像的数据进行了排序。155项检查（3%）具有1型ROP的参考标准诊断。i-ROP深度学习（DL）血管严重程度评分的受试者工作曲线下面积为0.960，用于检测1型ROP。建立阈值i-ROP DL评分为3赋予1型ROP 94%的灵敏度、79%的特异性、13%的阳性预测值和99.7%的阴性预测值。总体ROP严重程度的专家等级排序与i-ROP DL血管严重程度评分之间存在强相关性（斯皮尔曼相关系数= 0.93; p< 0.0001）。结论i-ROP DL系统仅在后极血管形态上进行训练后，以自动方式准确识别诊断类别和总体疾病严重程度。这些数据提供了概念证明，即深度学习筛查平台可以提高ROP诊断的客观性和筛查的可及性。
Background Prior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis.Methods Clinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1-9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity.Results 4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient= 0.93; p< 0.0001).Conclusion The i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.