Automated detection of early-stage ROP using a deep convolutional neural network

Yo Ping Huang, Haobijam Basanta, Eugene Yu Chuan Kang, Kuan Jen Chen, Yih Shiou Hwang, Chi Chun Lai, John P. Campbell, Michael F. Chiang, Robison Vernon Paul Chan, Shunji Kusaka, Yoko Fukushima, Wei Chi Wu*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

19 Scopus citations


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.

Original languageEnglish
Pages (from-to)1099-1103
Number of pages5
JournalBritish Journal of Ophthalmology
Issue number8
StatePublished - 01 08 2021
Externally publishedYes

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  • Retina
  • Telemedicine


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