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Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity
  1. Travis K Redd1,
  2. John Peter Campbell1,
  3. James M Brown2,
  4. Sang Jin Kim1,3,
  5. Susan Ostmo1,
  6. Robison Vernon Paul Chan4,
  7. Jennifer Dy5,
  8. Deniz Erdogmus5,
  9. Stratis Ioannidis5,
  10. Jayashree Kalpathy-Cramer2,
  11. Michael F Chiang1,6
  12. for the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium
    1. 1 Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA
    2. 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Maryland, USA
    3. 3 Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
    4. 4 Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois, USA
    5. 5 Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, USA
    6. 6 Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
    1. Correspondence to Michael F Chiang, Departments of Ophthalmology & Medical Informatics and Clinical Epidemiology, Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA; chiangm{at}ohsu.edu

    Abstract

    Background Prior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis.

    Methods Clinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect 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 combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity.

    Results 4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001).

    Conclusion The i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.

    • child health (paediatrics)
    • public health
    • retina
    • telemedicine

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    Footnotes

    • Contributors JB, JD, DE, SI and JKC developed the artificial intelligence system evaluated in this study. TR, JPC and MFC conceived the study design and drafted the manuscript. SO and JPC gathered, cleaned and organised the data. TR performed all data analysis. JB, SJK, RVPC and JKC performed critical revision of the manuscript.

    • Funding Supported by grants R01EY19474, K12 EY027720, P30EY10572, and P30EY001792 from the National Institutes of Health (Bethesda, Maryland, USA) by grants SCH-1622679, SCH-1622542, and SCH-1622536 from the National Science Foundation (Arlington, Virginia, USA), and by unrestricted departmental funding from Research to Prevent Blindness (New York, New York , USA).

    • Competing interests MFC is an unpaid member of the Scientific Advisory Board for Clarity Medical Systems (Pleasanton, California, USA), a Consultant for Novartis (Basel, Switzerland) and an initial member of Inteleretina, LLC (Honolulu, Hawaii, USA). RVPC is a Scientific Advisory Board member for Visunex Medical Systems (Fremont, California, USA) and a Consultant for Alcon (Fort Worth, Texas, USA), Allergan (Irvine, California, USA) and Bausch and Lomb (St. Louis, Missouri, USA). JPC is a consultant to Allergan (Irvine, California, USA).

    • Patient consent Patient/gaurdian consent obtained.

    • Ethics approval Oregon Health and Science University Institutional Review Board.

    • Provenance and peer review Not commissioned; externally peer reviewed.

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