Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity

Travis K. Redd, John Campbell, James M. Brown, Sang Jin Kim, Susan Ostmo, Robison Vernon Paul Chan, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis, Jayashree Kalpathy-Cramer, Michael Chiang

Research output: Contribution to journalArticle

15 Scopus citations


Background: Prior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis. Methods: Clinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1-9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity. Results: 4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001). Conclusion: The i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.

Original languageEnglish (US)
JournalBritish Journal of Ophthalmology
StateAccepted/In press - Jan 1 2018


  • child health (paediatrics)
  • public health
  • retina
  • telemedicine

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience

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    Redd, T. K., Campbell, J., Brown, J. M., Kim, S. J., Ostmo, S., Chan, R. V. P., Dy, J., Erdogmus, D., Ioannidis, S., Kalpathy-Cramer, J., & Chiang, M. (Accepted/In press). Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. British Journal of Ophthalmology. https://doi.org/10.1136/bjophthalmol-2018-313156