July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Automated Computer-Based Image Analysis in Monitoring Disease Progression for Retinopathy of Prematurity
Author Affiliations & Notes
  • Stanford Taylor
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Kishan Gupta
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • J. Peter Campbell
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • James M Brown
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlstown, Massachusetts, United States
  • Susan Ostmo
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Robison Vernon Paul Chan
    Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois, United States
  • Jennifer Dy
    Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Stratis Ioannidis
    Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Jayashree Kalpathy-Cramer
    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlstown, Massachusetts, United States
    Massachusetts General Hospital & Brigham and Women’s Hospital Center for Clinical Data Science, Boston, Massachusetts, United States
  • Sang Jin Kim
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Michael F Chiang
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Stanford Taylor, None; Kishan Gupta, None; J. Peter Campbell, None; James Brown, None; Susan Ostmo, None; Robison Chan, Alcon (C), Allergan (C), Bausch & Lomb (C), Visunex (C); Jennifer Dy, None; Stratis Ioannidis, None; Jayashree Kalpathy-Cramer, INFOTECH Soft Inc (C); Sang Kim, None; Michael Chiang, Clarity Medical Systems (S), National Institutes of Health (F), National Science Foundation (F), Novartis (C)
  • Footnotes
    Support  1. R01 EY019474 from the National Institutes of Health, Bethesda, Maryland; 2. P30EY010572 from the National Institutes of Health, Bethesda, Maryland; 3. NSF SCH-1622679
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 3937. doi:
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      Stanford Taylor, Kishan Gupta, J. Peter Campbell, James M Brown, Susan Ostmo, Robison Vernon Paul Chan, Jennifer Dy, Stratis Ioannidis, Jayashree Kalpathy-Cramer, Sang Jin Kim, Michael F Chiang; Automated Computer-Based Image Analysis in Monitoring Disease Progression for Retinopathy of Prematurity. Invest. Ophthalmol. Vis. Sci. 2018;59(9):3937.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To apply an automated deep learning algorithm in objectively monitoring retinopathy of prematurity (ROP) progression using a large cohort of babies born prematurely.

Methods : Images from clinical exams performed between July 2011 and December 2016 of infants in the multicenter Imaging & Informatics in ROP (i-ROP) study were reviewed. A previously validated computer-based deep learning algorithm was used to grade the exam images and a continuous score (i-ROP score) from 1 (normal retinal vasculature) to 9 (severe plus disease) was assigned to each exam. Infants were subdivided using a reference standard diagnosis based on clinical exam and expert grading of clinical images into groupings based on need for treatment. The disease course was then assessed between the two groups longitudinally across multiple exams until the infants were lost to follow-up, the retina fully vascularized, or treatment-requiring ROP developed.

Results : A total of 5255 clinical examinations in 871 babies (1692 eyes) were analyzed. 91 eyes (5.4% of the cohort) progressed to treatment-requiring disease, which occurred at an average i-ROP grade of 5.80 and 37.6 weeks post-menstrual age (PMA). The mean gestational age and weight at birth of those that required treatment was 25.1 weeks and 683 grams, in contrast to 27.3 weeks and 966 grams in those that did not require treatment (p<0.05 for both comparisons). Average i-ROP scores of the two groups were significantly different at all time-points analyzed but became more apparent with advancing PMA. At 36-38 week exams, the average i-ROP score of infants developing treatment-requiring disease was 5.22 compared to 1.20 in those that received no treatment (p<0.05). When assessing the rate of change in i-ROP scores over time, eyes that developed treatment-requiring disease had a faster average increase in i-ROP score at a given time (0.4 – 1.32 i-ROP points/week) than untreated eyes (0.07 – 0.20 i-ROP points/week) (p<0.05).

Conclusions : The i-ROP score, an automated algorithm using deep learning, effectively correlates with observed clinical progression of ROP in this cohort of infants born prematurely. Going forward, automated computer-based image analysis may be considered as a means to monitor disease progression in infants undergoing screening for ROP.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

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