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Original Investigation
July 3, 2019

Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning

Author Affiliations
  • 1Department of Ophthalmology, Casey Eye institute, Oregon Health & Science University, Portland
  • 2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
  • 3Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
  • 4Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
  • 5Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
  • 6Massachusetts General Hospital and Brigham and Women’s Hospital Center for Clinical Data Science, Boston
  • 7Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland
JAMA Ophthalmol. 2019;137(9):1022-1028. doi:10.1001/jamaophthalmol.2019.2433
Key Points

Question  Can a quantitative measurement of retinopathy of prematurity severity be tracked over time to identify disease progression?

Findings  In this cohort study of 871 infants with 5255 clinical examinations, a quantitative retinopathy of prematurity vascular severity score developed using an automated deep learning–based plus disease algorithm identified differences in the mean severity of eyes progressing to treatment-requiring retinopathy of prematurity compared with eyes that did not require treatment using only a posterior pole photograph.

Meaning  Tracking quantitative measurements of retinopathy of prematurity severity may be an effective method of identifying patients at risk for disease progression and in need of future retinopathy of prematurity treatment.


Importance  Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective and qualitative.

Objective  To describe a quantitative ROP severity score derived using a deep learning algorithm designed to evaluate plus disease and to assess its utility for objectively monitoring ROP progression.

Design, Setting, and Participants  This retrospective cohort study included images from 5255 clinical examinations of 871 premature infants who met the ROP screening criteria of the Imaging and Informatics in ROP (i-ROP) Consortium, which comprises 9 tertiary care centers in North America, from July 1, 2011, to December 31, 2016. Data analysis was performed from July 2017 to May 2018.

Exposure  A deep learning algorithm was used to assign a continuous ROP vascular severity score from 1 (most normal) to 9 (most severe) at each examination based on a single posterior photograph compared with a reference standard diagnosis (RSD) simplified into 4 categories: no ROP, mild ROP, type 2 ROP or pre-plus disease, or type 1 ROP. Disease course was assessed longitudinally across multiple examinations for all patients.

Main Outcomes and Measures  Mean ROP vascular severity score progression over time compared with the RSD.

Results  A total of 5255 clinical examinations from 871 infants (mean [SD] gestational age, 27.0 [2.0] weeks; 493 [56.6%] male; mean [SD] birth weight, 949 [271] g) were analyzed. The median severity scores for each category were as follows: 1.1 (interquartile range [IQR], 1.0-1.5) (no ROP), 1.5 (IQR, 1.1-3.4) (mild ROP), 4.6 (IQR, 2.4-5.3) (type 2 and pre-plus), and 7.5 (IQR, 5.0-8.7) (treatment-requiring ROP) (P < .001). When the long-term differences in the median severity scores across time between the eyes progressing to treatment and those who did not eventually require treatment were compared, the median score was higher in the treatment group by 0.06 at 30 to 32 weeks, 0.75 at 32 to 34 weeks, 3.56 at 34 to 36 weeks, 3.71 at 36 to 38 weeks, and 3.24 at 38 to 40 weeks postmenstrual age (P < .001 for all comparisons).

Conclusions and Relevance  The findings suggest that the proposed ROP vascular severity score is associated with category of disease at a given point in time and clinical progression of ROP in premature infants. Automated image analysis may be used to quantify clinical disease progression and identify infants at high risk for eventually developing treatment-requiring ROP. This finding has implications for quality and delivery of ROP care and for future approaches to disease classification.