July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Monitoring response to treatment in severe retinopathy of prematurity using a deep learning based quantitative severity scale
Author Affiliations & Notes
  • Kishan Gupta
    Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, United States
  • J. Peter Campbell
    Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, United States
  • Stanford Taylor
    Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, United States
  • James M Brown
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
  • Susan Ostmo
    Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, United States
  • R.V. Paul Chan
    Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois, United States
  • Jennifer Dy
    Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Deniz Erdogmus
    Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Stratis Ioannidis
    Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Jayashree Kalpathy-Cramer
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
    Massachusetts General Hospital & Brigham and Women’s Hospital Center for Clinical Data Science, Boston, Massachusetts, United States
  • Sang Jin Kim
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
  • Michael F Chiang
    Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, United States
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Kishan Gupta, None; J. Peter Campbell, None; Stanford Taylor, None; James Brown, None; Susan Ostmo, None; R.V. Chan, None; Jennifer Dy, None; Deniz Erdogmus, 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, 3766. doi:
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      Kishan Gupta, J. Peter Campbell, Stanford Taylor, James M Brown, Susan Ostmo, R.V. Paul Chan, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis, Jayashree Kalpathy-Cramer, Sang Jin Kim, Michael F Chiang; Monitoring response to treatment in severe retinopathy of prematurity using a deep learning based quantitative severity scale. Invest. Ophthalmol. Vis. Sci. 2018;59(9):3766.

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

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Abstract

Purpose : To evaluate the clinical utility of quantitative image analysis using a deep learning plus disease severity score to monitor response to treatment in patients with type 1 (treatment-requiring) retinopathy of prematurity (ROP).

Methods : Images from clinical exams performed between July 2011 and December 2016 of infants in the multicenter Imaging and Informatics in ROP (i-ROP) study were reviewed to identify babies with type 1 ROP. Using the output of a fully automated deep learning algorithm trained to evaluate plus disease severity, we developed a continuous score (i-ROP score) from 1 (normal retinal vasculature) to 9 (severe plus disease). We examined the pre-treatment and post-treatment i-ROP score for the four weeks preceding and following treatment with either laser or intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF). Eyes were only included if they had at least one clinical exam before and after treatment.

Results : A total of 43 eyes received treatment with either laser (n=34) or anti-VEGF therapy (n=9). The mean i-ROP score 2 weeks prior to treatment was 3.9, compared to 7.1 at time of treatment (p< 0.0001, student's t-test). Two weeks post treatment, the mean score decreased to 4.0 (p < 0.0001, student's t-test).

Conclusions : The i-ROP score, an automated ROP severity score developed using deep learning, correlates with observed clinical progression and regression of disease in ROP. Using this technology, it may be able to objectively monitor ROP response to treatment. Future work is needed to determine whether there may be observed differences in response to treatment between treatment groups, and whether this technology may identify disease recurrence requiring retreatment.

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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