July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Quantitative analysis of aggressive posterior retinopathy of prematurity using deep learning
Author Affiliations & Notes
  • Kellyn N Smith
    Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Sang Jin Kim
    Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
    Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (the Republic of)
  • Isaac Goldstein
    Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Susan Ostmo
    Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • RV Paul Chan
    Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois, United States
  • James Martin Brown
    Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
  • Jayashree Kalpathy-Cramer
    Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
    Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science, Boston, Massachusetts, United States
  • J. Peter Campbell
    Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Michael F Chiang
    Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
    Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Kellyn Smith, None; Sang Jin Kim, None; Isaac Goldstein, None; Susan Ostmo, None; RV Paul Chan, Visunex Medical Systems (Fremont, CA) (C); James Brown, None; Jayashree Kalpathy-Cramer, None; J. Peter Campbell, None; Michael Chiang, Inteleretina, LLC (Honolulu, HI) (I), Novartis (Basel, Switzerland) (C), Scientific Advisory Board for Clarity Medical Systems (Pleasanton, CA) (S)
  • Footnotes
    Support  Supported by National Institutes of Health grants R01EY19474, P30EY10572, K12EY27720, and P30EY001792 (Bethesda, MD), National Science Foundation grants SCH-1622679 (Arlington, VA) and SCH-1622542, and unrestricted departmental funding from Research to Prevent Blindness (New York, NY)
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 4759. doi:
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    • Get Citation

      Kellyn N Smith, Sang Jin Kim, Isaac Goldstein, Susan Ostmo, RV Paul Chan, James Martin Brown, Jayashree Kalpathy-Cramer, J. Peter Campbell, Michael F Chiang; Quantitative analysis of aggressive posterior retinopathy of prematurity using deep learning. Invest. Ophthalmol. Vis. Sci. 2019;60(9):4759.

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

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Abstract

Purpose : Aggressive posterior retinopathy of prematurity (APROP) is a severe form of ROP with a higher rate of progression to retinal detachment and permanent vision loss. The purpose of this study was to quantitatively characterize APROP using a deep learning (DL) based ROP severity score.

Methods : We conducted a retrospective analysis of the Imaging and Informatics in ROP (i-ROP) cohort from 7 North American centers, consisting of 1029 total patients and 7264 clinical eye exams. A reference standard diagnosis (RSD) was generated for each eye exam using methods previously published combining three independent image-based and one ophthalmoscopic gradings. Eyes without a history of ROP treatment were then categorized by RSD into five ROP severity groups: None, Mild, Type 2 or Pre-Plus, Treatment-Requiring (TR) without APROP, TR with APROP. All images were analyzed using a previously-published deep learning system (i-ROP DL) and assigned a vascular severity score from 1-9. Demographic data, the presence of systemic comorbidities, and the post-menstrual age at peak disease severity were evaluated for each category.

Results : The Table displays the results of the demographics of the 5 ROP severity categories. Infants with TR ROP who developed APROP tended to be more premature by birth weight (617 g vs 736 g, p>0.05) and gestational age (24.3 wks vs 25.5 wks, p<0.01) than those with only TR ROP without APROP. Additionally, these infants reached peak severity at earlier mean postmenstrual age (PMA) than those with only TR ROP without APROP (34.7 wks vs 39.0 wks, p<0.001; Table). The mean i-ROP vascular severity score correlated with the RSD-based ROP severity categories (Figure).

Conclusions : Premature infants in North America with APROP are born younger and develop disease earlier than infants with other categories of ROP, including other infants with treatment-requiring disease. Disease severity is quantifiable with i-ROP score, which correlates with clinically identified categories of disease including APROP. DL based ROP screening may identify these infants developing APROP prior to peak disease severity potentially allowing for earlier treatment in the future.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Table. Measures of prematurity show difference between TR without APROP and TR with APROP

Table. Measures of prematurity show difference between TR without APROP and TR with APROP

 

Figure. Vascular severity score correlates with disease severity (median i-ROP with interquartile ranges)

Figure. Vascular severity score correlates with disease severity (median i-ROP with interquartile ranges)

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