TY - JOUR T1 - Automated detection of early-stage ROP using a deep convolutional neural network JF - British Journal of Ophthalmology JO - Br J Ophthalmol SP - 1099 LP - 1103 DO - 10.1136/bjophthalmol-2020-316526 VL - 105 IS - 8 AU - Yo-Ping Huang AU - Haobijam Basanta AU - Eugene Yu-Chuan Kang AU - Kuan-Jen Chen AU - Yih-Shiou Hwang AU - Chi-Chun Lai AU - John P Campbell AU - Michael F Chiang AU - Robison Vernon Paul Chan AU - Shunji Kusaka AU - Yoko Fukushima AU - Wei-Chi Wu Y1 - 2021/08/01 UR - http://bjo.bmj.com/content/105/8/1099.abstract N2 - Background/Aim To automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).Methods This retrospective cross-sectional study was conducted in a referral medical centre in Taiwan. Only premature infants with no ROP, stage 1 ROP or stage 2 ROP were enrolled. Overall, 11 372 retinal fundus images were compiled and split into 10 235 images (90%) for training, 1137 (10%) for validation and 244 for testing. A deep CNN was implemented to classify images according to the ROP stage. Data were collected from December 17, 2013 to May 24, 2019 and analysed from December 2018 to January 2020. The metrics of sensitivity, specificity and area under the receiver operating characteristic curve were adopted to evaluate the performance of the algorithm relative to the reference standard diagnosis.Results The model was trained using fivefold cross-validation, yielding an average accuracy of 99.93%±0.03 during training and 92.23%±1.39 during testing. The sensitivity and specificity scores of the model were 96.14%±0.87 and 95.95%±0.48, 91.82%±2.03 and 94.50%±0.71, and 89.81%±1.82 and 98.99%±0.40 when predicting no ROP versus ROP, stage 1 ROP versus no ROP and stage 2 ROP, and stage 2 ROP versus no ROP and stage 1 ROP, respectively.Conclusions The proposed system can accurately differentiate among ROP early stages and has the potential to help ophthalmologists classify ROP at an early stage. ER -