July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Artificial intelligence in retinopathy of prematurity: identification of clinically significant retinal vascular findings using computer-based image analysis
Author Affiliations & Notes
  • Michael F Chiang
    Ophthalmology and Medical Informatics, Oregon Health & Science University, Portland, Oregon, United States
  • James M Brown
    Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
  • Veysi Yildiz
    Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Peng Tian
    Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Layla Ghergherehchi
    Ophthalmology, Oregon Health & Science University, Portland, Oregon, United States
  • J. Peter Campbell
    Ophthalmology, Oregon Health & Science University, Portland, Oregon, United States
  • Susan Ostmo
    Ophthalmology, Oregon Health & Science University, Portland, Oregon, United States
  • Sang Jin Kim
    Ophthalmology, Oregon Health & Science University, Portland, Oregon, United States
    Ophthalmology, Samsung Medical Center, Seoul, Korea (the Republic of)
  • Robison Vernon Paul Chan
    Ophthalmology, University of Illinois at Chicago, Chicago, Illinois, United States
  • Jennifer Dy
    Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States
  • Deniz Erdogmus
    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
    Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Michael Chiang, Clarity Medical Systems (S), National Eye Institute (F), National Science Foundation (F), Novartis (C); James Brown, None; Veysi Yildiz, None; Peng Tian, None; Layla Ghergherehchi, None; J. Peter Campbell, None; Susan Ostmo, None; Sang Kim, None; Robison Chan, Alcon (C), Allergan (C), Bausch and Lomb (C), Visunex (C); Jennifer Dy, None; Deniz Erdogmus, None; Stratis Ioannidis, None; Jayashree Kalpathy-Cramer, INFOTECH Soft Inc. (C)
  • Footnotes
    Support  Supported by NIH (R01EY019474, P30EY10572, P41EB015896), NSF (SCH-1622542 at MGH; SCH-1622536 at Northeastern; SCH-1622679 at OHSU), and by unrestricted departmental funding from Research to Prevent Blindness (OHSU).
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 2764. doi:
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    • Get Citation

      Michael F Chiang, James M Brown, Veysi Yildiz, Peng Tian, Layla Ghergherehchi, J. Peter Campbell, Susan Ostmo, Sang Jin Kim, Robison Vernon Paul Chan, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis, Jayashree Kalpathy-Cramer; Artificial intelligence in retinopathy of prematurity: identification of clinically significant retinal vascular findings using computer-based image analysis. Invest. Ophthalmol. Vis. Sci. 2018;59(9):2764.

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

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Abstract

Purpose : Plus disease is the key parameter for identifying infants at risk for blindness from ROP, and is defined as arterial tortuosity and venous dilation in the posterior pole. Studies have shown that: (1) diagnosis is subjective and variable, (2) experts often consider vascular features beyond those in the definition and are often unable to explain their diagnostic process, and (3) computer-based analysis can diagnose plus disease with comparable or better accuracy than experts. This study uses outputs from image analysis systems to identify vascular features considered most significant by experts for plus disease diagnosis.

Methods : 31 wide-angle retinal images with plus disease, based on a consensus reference standard, were analyzed using (a) a deep learning system (i-ROP DL) performing occlusion analysis with a convolutional neural network to compute the significance of each 12x12 pixel region to the network’s diagnostic ability, visualized as a heatmap (Figure) from which vascular features were extracted that most affected the probability of diagnosis; (b) a supervised machine learning (SML) system (i-ROP ASSIST) that quantified 50 different retinal vascular features, from which the ones most closely associated with the reference standard diagnosis were identified.

Results : Retinal features identified as important for plus disease diagnosis based on deep learning occlusion analysis were: (1) central retinal vessel location (31/31 images), (2) mid-peripheral vessel location (25/31 images), (3) arterial tortuosity (31/31 images), (4) venous dilation (31/31 images), (5) arterial dilation (31/31), (6) venous tortuosity (31/31). SML feature output identified the following features as most important: (1) cumulative tortuosity index (30/31), (2) acceleration or rate of change of direction of vessel (28/31), and (3) average point diameter (23/31).

Conclusions : Backward reasoning from outputs of computer-based image analysis systems shows that experts consider numerous vascular features during plus disease diagnosis, including dilation and tortuosity of both arteries and veins in all fields of view. This has important implications for clinical care and education in ROP, and this methodology may be applied to other ophthalmic diseases.

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

 

Heatmap identifying key regions for plus disease diagnosis using deep learning occlusion analysis.

Heatmap identifying key regions for plus disease diagnosis using deep learning occlusion analysis.

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