July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Machine Learning for Prediction of Retinopathy of Prematurity Fundus Image Quality from Clinical Data
Author Affiliations & Notes
  • Aaron S Coyner
    Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
    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, Samsung Medical Center, Seoul, Korea (the Democratic People's Republic of)
    Ophthalmology, Oregon Health & Science University, Portland, Oregon, United States
  • Karyn E Jonas
    Ophthalmology, Illinois Eye and Ear Infirmary, Chicago, Illinois, United States
  • RV Paul Chan
    Ophthalmology, Illinois Eye and Ear Infirmary, Chicago, Illinois, United States
  • Michael F Chiang
    Ophthalmology, Oregon Health & Science University, Portland, Oregon, United States
    Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Aaron Coyner, None; J. Peter Campbell, None; Susan Ostmo, None; Sang Jin Kim, None; Karyn Jonas, None; RV Paul Chan, Visunex Medical Systems (C); Michael Chiang, Clarity Medical Systems (S), Inteleretina (I), Novartis (C)
  • Footnotes
    Support  Supported by National Institutes of Health grants T15LM007088, P30 EY001792, P30EY10572, R01EY19474 and K12EY27720 (Bethesda, MD), National Science Foundation grant SCH-1622679 (Arlington, VA), and unrestricted departmental funding from Research to Prevent Blindness (New York, NY).
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1525. doi:
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    • Get Citation

      Aaron S Coyner, J. Peter Campbell, Susan Ostmo, Sang Jin Kim, Karyn E Jonas, RV Paul Chan, Michael F Chiang; Machine Learning for Prediction of Retinopathy of Prematurity Fundus Image Quality from Clinical Data. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1525.

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

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Abstract

Purpose : Telemedicine and artificial intelligence have shown great promise for improving management of ophthalmic diseases such as retinopathy of prematurity (ROP). A significant challenge in their implementation is the need for high-quality images. We have previously trained a convolutional neural network to detect Acceptable Quality (AQ) images from those that are not. However, it remains unclear as to what degree image quality is related to clinical factors versus imaging technique. The aim of this study is to identify what clinical factors contribute to image quality and to predict image quality with them.

Methods : We reviewed 4,030 examinations from the Imaging and Informatics in ROP (i-ROP) cohort study, in which images were obtained (Retcam, Natus Medical Systems, Pleasanton, CA) during routine ROP screenings. Six experts assessed image quality, voting AQ, Possibly AQ (PAQ) or Not AQ (NAQ). Each expert voted on each image, and a majority vote was used for the image quality label. The data set was split into separate training (75%) and test sets (25%). A random forest classifier (RF) was trained, via 5-fold cross-validation, to classify AQ images from non-AQ images. Variables with a mean decrease in Gini greater than 0.5 were considered significant. To confirm variable importance, logistic regression was performed, and variables with a p-value less than 0.05 were considered significant. A new RF was trained using only the significant predictors identified by both models and its performance was evaluated on the test set.

Results : The variables post-menstrual age, gestational age, race, site (different imaging technician at each site), and session (number of times a baby was imaged) were identified as significant predictors of image quality by both models (Figure 1). Using only these predictors, a RF classified AQ images from non-AQ images with an area under the receiver operating characteristics curve (AUC) of 0.875 on a test set, with a sensitivity and specificity of 86% and 76%, respectively (Figure 2).

Conclusions : Clinical and demographic factors can be used to predict retinal fundus image quality in ROP better than chance. This suggests that poor image quality is partially dependent upon the clinical demographics of the subject being imaged.

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

 

Descriptive plots of clinical factors found to be important for image quality.

Descriptive plots of clinical factors found to be important for image quality.

 

Evaluation of final RF model on the test set via ROC curve. AUC = 0.875.

Evaluation of final RF model on the test set via ROC curve. AUC = 0.875.

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