July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Application of a Quantitative Image Analysis Scale Using Deep Learning for Detection of Clinically Significant ROP
Author Affiliations & Notes
  • Travis Redd
    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, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
  • Sang Jin Kim
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Susan Ostmo
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Robison Vernon 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
    Center for Clinical Data Science, Massachussets General Hospital & Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Michael F Chiang
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
    Department of Medical Informatics and Clinical Epidemiology, Oregon Healthy & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Travis Redd, None; J. Peter Campbell, None; James Brown, None; Sang Kim, None; Susan Ostmo, None; Robison Chan, None; Jennifer Dy, None; Deniz Erdogmus, None; Stratis Ioannidis, None; Jayashree Kalpathy-Cramer, INFOTECH soft inc. (C); Michael Chiang, Clarity Medical Systems (S), National Institutes of Health (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, 2782. doi:
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    • Get Citation

      Travis Redd, J. Peter Campbell, James M Brown, Sang Jin Kim, Susan Ostmo, Robison Vernon Paul Chan, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis, Jayashree Kalpathy-Cramer, Michael F Chiang; Application of a Quantitative Image Analysis Scale Using Deep Learning for Detection of Clinically Significant ROP. Invest. Ophthalmol. Vis. Sci. 2018;59(9):2782.

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

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Abstract

Purpose : Retinopathy of prematurity (ROP) is a disease of preterm infants with significant visual morbidity and inadequate access to screening. Telemedicine using computerized image assessment offers a compelling opportunity to efficiently address this gap. We have previously demonstrated the near-perfect accuracy of a deep learning computer-generated severity score for diagnosing plus disease. Here we assess the clinical utility of this scoring system by evaluating its applicability to all parameters of ROP diagnosis, including zone, stage, and overall disease category.

Methods : Clinical examination and fundus photography were performed on at-risk infants from 7 participating centers. A deep learning based system was developed by training on detection of 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 using methods previously published. The area under the receiver operating curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of this score for the detection of clinically significant ROP were then determined.

Results : A total of 5,219 eye examinations from 871 infants were analyzed. 1,100 exams demonstrated type 2 or worse ROP, including 164 with type 1 ROP. The i-ROP plus score had an AUROC of 0.906 for detection of type 2 or worse ROP, and 0.949 for detection of type 1 ROP (Table). A score of 2.3 conferred 85% sensitivity, 81% specificity, 54% PPV, and 95% NPV for type 2 ROP or worse. A score of 4.8 had 88% sensitivity, 88% specificity, 19% PPV, and 99.6% NPV for type 1 ROP. The i-ROP plus score was slightly less effective at detecting stage 3 disease (AUROC=0.864) and zone I disease (AUROC=0.719).

Conclusions : Despite only being trained to recognize plus disease, this system has high accuracy for detecting clinically significant (type 2 or worse) ROP and fair accuracy for detecting stage 3 ROP. This confirms the clinical utility of a deep learning image assessment system for ROP diagnosis, with potential applications for disease screening in resource-limited settings. Future work focusing on training a deep learning algorithm to specifically identify zone and stage may lead to a fully automated system that can diagnose ROP as well as clinical examiners.

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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