Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks
Ling‐Ping Cen, Jie Ji, Jianwei Lin, Si-Tong Ju, Hong-Jie Lin, Tai-Ping Li, Yun Wang, Jian-Feng Yang, Yu-Fen Liu, Shaoying Tan, Li Xuan Tan, Dongjie Li, Yifan Wang, Dezhi Zheng, Yongqun Xiong, Hanfu Wu, Jingjing Jiang, Zhenggen Wu, Dingguo Huang, Tingkun Shi, Binyao Chen, Jianling Yang, Xiaoling Zhang, Li Luo, Chukai Huang, Guihua Zhang, Yuqiang Huang, Tsz Kin Ng, Haoyu Chen, Weiqi Chen, Chi Pui Pang, Mingzhi Zhang
Abstract
Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.