July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Artificial intelligence using a deep learning system with transfer learning to predict refractive error and myopic macular degeneration from color fundus photographs
Author Affiliations & Notes
  • Tien-En Tan
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Daniel SW Ting
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Yong Liu
    Institute of High Performance Computing, Singapore
  • Shaohua Li
    Institute of High Performance Computing, Singapore
  • Cheng Chen
    Institute of High Performance Computing, Singapore
  • Quang Nguyen
    Singapore Eye Research Institute, Singapore
  • Chee Wai Wong
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Quan V Hoang
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Shu Yen Lee
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Edmund Yick Mun Wong
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Ian Yew San Yeo
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Yee Ling Wong
    Saw Swee Hock School of Public Health, Singapore
    Singapore Eye Research Institute, Singapore
  • Ching-Yu Cheng
    Singapore Eye Research Institute, Singapore
    Singapore National Eye Centre, Singapore, Singapore
  • Seang-Mei Saw
    Saw Swee Hock School of Public Health, Singapore
    Singapore Eye Research Institute, Singapore
  • Gemmy Chui Ming Cheung
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Tien Yin Wong
    Singapore National Eye Centre, Singapore, Singapore
    Singapore Eye Research Institute, Singapore
  • Footnotes
    Commercial Relationships   Tien-En Tan, None; Daniel Ting, None; Yong Liu, None; Shaohua Li, None; Cheng Chen, None; Quang Nguyen, None; Chee Wai Wong, None; Quan Hoang, None; Shu Yen Lee, None; Edmund Wong, None; Ian Yeo, None; Yee Ling Wong, None; Ching-Yu Cheng, None; Seang-Mei Saw, None; Gemmy Cheung, None; Tien Wong, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1478. doi:
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      Tien-En Tan, Daniel SW Ting, Yong Liu, Shaohua Li, Cheng Chen, Quang Nguyen, Chee Wai Wong, Quan V Hoang, Shu Yen Lee, Edmund Yick Mun Wong, Ian Yew San Yeo, Yee Ling Wong, Ching-Yu Cheng, Seang-Mei Saw, Gemmy Chui Ming Cheung, Tien Yin Wong; Artificial intelligence using a deep learning system with transfer learning to predict refractive error and myopic macular degeneration from color fundus photographs. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1478.

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

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Abstract

Purpose : The global burden of myopia is significant, and increasing. Patients with high myopia are at increased risk of vision-threatening complications, including myopic macular degeneration (MMD). We developed an artificial intelligence (AI) based deep learning system (DLS) using fundus photographs to predict refractive error, high myopia, and MMD.

Methods : The DLS consisted of a convolutional neural network (DenseNet) pre-trained with XGBoost algorithm, developed using retinal images from a multiethnic population-based cohort: the Singapore Epidemiology of Eye Diseases (SEED) cohort, with 15,876 (12,703 for training, 3,173 for testing) and 16,793 (13,433 for training, 3,360 for testing) images used for prediction of refractive error and MMD respectively. Images were classified by professional graders according to the International Meta-Analysis for Pathologic Myopia (META-PM) classification into 5 categories (grades 0 to 4), with MMD defined as category 2 or worse. The DLS was assessed in its ability to (1) predict spherical equivalent (SE), (2) detect high myopia (SE ≤ -6.00D), and (3) detect MMD. The area under the receiver operating characteristic (ROC) curve (AUC) was determined for these classifications.

Results : The DLS was able to predict SE with a mean absolute error (MAE) of 1.20D. The AUC, sensitivity and specificity (at the specified threshold) for detection of high myopia were 0.942, 91.7% and 83.5% respectively. The AUC, sensitivity and specificity for detection of MMD were 0.955, 91.9% and 87.7% respectively. The ROC curves are shown in Figure 1.

Conclusions : Using fundus photographs, the DLS developed was able to predict refractive error, and detect high myopia and MMD with a high degree of accuracy. Application of this DLS in screening programs could allow early identification of patients at high-risk for vision-threatening complications of myopia.

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

 

Figure 1. Receiver operating characteristic curves for detection of (A) high myopia and (B) myopic macular degeneration.

Figure 1. Receiver operating characteristic curves for detection of (A) high myopia and (B) myopic macular degeneration.

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