Author:
Jiang Jiewei,Liu Wei,Pei Mengjie,Guo Liufei,Yang Jingshi,Wu Chengchao,Lu Jiaojiao,Gao Ruijie,Chen Wei,Gong Jiamin,Zhu Mingmin,Li Zhongwen
Abstract
AbstractKeratitis is a major cause of corneal blindness worldwide. Early identification and timely treatment of keratitis can deter the disease progression, reaching a better prognosis. The diagnosis of keratitis often requires professional ophthalmologists. However, ophthalmologists are relatively scarce and unevenly distributed, especially in underserved and remote regions, making the early diagnosis of keratitis challenging. In this study, an object localization method combined with cost-sensitive deep attention convolutional neural network (OL-CDACNN) was proposed for the automated diagnosis of keratitis. First, the single shot multibox detector (SSD) algorithm was employed to automatically locate the region of conjunctiva and cornea (Conj_Cor) on the original slit-lamp image. Then, the region of Conj_Cor was classified using a cost-sensitive deep attention convolutional network (CDACNN) to identify keratitis, other cornea abnormalities, and normal cornea. A total of 12,407 slit-lamp images collected from four clinical institutions were used to develop and evaluate the OL-CDACNN. For detecting keratitis, other cornea abnormalities, and normal cornea, the OL-CDACNN model achieved area under the receiver operating characteristic curves (AUCs) of 0.998, 0.997, and 1.000, respectively, in an internal test dataset. The comparable performance (AUCs ranged from 0.981 to 0.998) was observed in three external test datasets, further verifying its effectiveness and generalizability. Due to reliable performance, our model has a high potential to provide an accurate diagnosis and prompt referral for a patient with keratitis in an automated fashion.
Funder
Natural Science Basic Research Program of Shaanxi Province
National Natural Science Foundation of China
Postgraduate Innovation Fund of Xi'an University of Posts and Telecommunications
International Science and Technology Cooperation Program Project Shaanxi Province Key Research and Development Program
Natural Science Foundation of Zhejiang Province
Medical Science and Technology Project of Zhejiang Province
Ningbo Science & technology program
Publisher
Springer Science and Business Media LLC
Subject
Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems