State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China.
Guangzhou Healgoo Interactive Medical Technology Co. Ltd., Guangzhou, China.
Ophthalmology. 2018 Aug;125(8):1199-1206. doi: 10.1016/j.ophtha.2018.01.023. Epub 2018 Mar 2.
To assess the performance of a deep learning algorithm for detecting referable glaucomatous optic neuropathy (GON) based on color fundus photographs.
A deep learning system for the classification of GON was developed for automated classification of GON on color fundus photographs.
We retrospectively included 48 116 fundus photographs for the development and validation of a deep learning algorithm.
This study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON. The reference standard was made until 3 graders achieved agreement. A separate validation dataset of 8000 fully gradable fundus photographs was used to assess the performance of this algorithm.
The area under receiver operator characteristic curve (AUC) with sensitivity and specificity was applied to evaluate the efficacy of the deep learning algorithm detecting referable GON.
In the validation dataset, this deep learning system achieved an AUC of 0.986 with sensitivity of 95.6% and specificity of 92.0%. The most common reasons for false-negative grading (n = 87) were GON with coexisting eye conditions (n = 44 [50.6%]), including pathologic or high myopia (n = 37 [42.6%]), diabetic retinopathy (n = 4 [4.6%]), and age-related macular degeneration (n = 3 [3.4%]). The leading reason for false-positive results (n = 480) was having other eye conditions (n = 458 [95.4%]), mainly including physiologic cupping (n = 267 [55.6%]). Misclassification as false-positive results amidst a normal-appearing fundus occurred in only 22 eyes (4.6%).
A deep learning system can detect referable GON with high sensitivity and specificity. Coexistence of high or pathologic myopia is the most common cause resulting in false-negative results. Physiologic cupping and pathologic myopia were the most common reasons for false-positive results.
评估基于眼底彩色照片检测可转诊青光眼视神经病变(GON)的深度学习算法的性能。
开发了一种用于 GON 分类的深度学习系统,用于对眼底彩色照片进行 GON 的自动分类。
我们回顾性纳入了 48116 张眼底照片,用于开发和验证深度学习算法。
本研究招募了 21 名经过培训的眼科医生来对照片进行分类。可转诊 GON 定义为垂直杯盘比为 0.7 或更高,以及其他典型的 GON 改变。参考标准是直到 3 位分级员达成一致。使用 8000 张完全可分级的眼底照片的单独验证数据集来评估该算法的性能。
应用受试者工作特征曲线(ROC)下面积(AUC)及其灵敏度和特异性来评估深度学习算法检测可转诊 GON 的效果。
在验证数据集中,该深度学习系统的 AUC 为 0.986,灵敏度为 95.6%,特异性为 92.0%。假阴性分级最常见的原因(n=87)是 GON 合并其他眼部疾病(n=44[50.6%]),包括病理性或高度近视(n=37[42.6%])、糖尿病视网膜病变(n=4[4.6%])和年龄相关性黄斑变性(n=3[3.4%])。假阳性结果的主要原因(n=480)是存在其他眼部疾病(n=458[95.4%]),主要包括生理性杯盘比增大(n=267[55.6%])。仅有 22 只眼睛(4.6%)错误分类为假阳性结果。
深度学习系统可以高灵敏度和特异性检测可转诊 GON。高度近视或病理性近视的并存是导致假阴性结果最常见的原因。生理性杯盘比增大和病理性近视是假阳性结果最常见的原因。