July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
The Application of Deep Learning System to Screen for Diabetic Retinopathy in an Underprivileged African Population with Diabetes
Author Affiliations & Notes
  • Valentina Bellemo
    Singapore Eye Research Intitute, Singapore
  • Zhan Wei Lim
    School of Computing, National University of Singapore, Singapore
  • Gilbert Lim
    School of Computing, National University of Singapore, Singapore
  • Quang Nguyen
    Singapore Eye Research Intitute, Singapore
  • Michelle Y.T. Yip
    Singapore Eye Research Intitute, Singapore
    Duke-NUS Medical School, Singapore
  • Yuchen Xie
    Singapore Eye Research Intitute, Singapore
  • Xin Qi Lee
    Singapore Eye Research Intitute, Singapore
  • Haslina Hamzah
    Singapore National Eye Centre, Singapore
  • Jinyi Ho
    Singapore National Eye Centre, Singapore
  • Gavin Siew Wei Tan
    Singapore Eye Research Intitute, Singapore
    Singapore National Eye Centre, Singapore
  • Wynne Hsu
    School of Computing, National University of Singapore, Singapore
  • Mong Li Lee
    School of Computing, National University of Singapore, Singapore
  • Sobha Sivaprasad
    Retina Department, Moorfields Eye Hospital, United Kingdom
  • Geeta Menon
    Department of Ophthalmology, Frimley Park Hospital, United Kingdom
  • Tien Yin Wong
    Singapore Eye Research Intitute, Singapore
    Singapore National Eye Centre, Singapore
  • Daniel SW Ting
    Singapore Eye Research Intitute, Singapore
    Singapore National Eye Centre, Singapore
  • Footnotes
    Commercial Relationships   Valentina Bellemo, None; Zhan Wei Lim, None; Gilbert Lim, EyRis (P); Quang Nguyen, None; Michelle Yip, None; Yuchen Xie, None; Xin Qi Lee, None; Haslina Hamzah, None; Jinyi Ho, None; Gavin Tan, None; Wynne Hsu, EyRis (P); Mong Li Lee, EyRis (P); Sobha Sivaprasad, EyRis (P); Geeta Menon, None; Tien Wong, EyRis (P); Daniel Ting, EyRis (P)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1439. doi:
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      Valentina Bellemo, Zhan Wei Lim, Gilbert Lim, Quang Nguyen, Michelle Y.T. Yip, Yuchen Xie, Xin Qi Lee, Haslina Hamzah, Jinyi Ho, Gavin Siew Wei Tan, Wynne Hsu, Mong Li Lee, Sobha Sivaprasad, Geeta Menon, Tien Yin Wong, Daniel SW Ting; The Application of Deep Learning System to Screen for Diabetic Retinopathy in an Underprivileged African Population with Diabetes. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1439.

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

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Abstract

Purpose : Zambia is a low-income developing country overwhelmed with widespread rural poverty, ranked 191th for gross domestic product per capita in 2018. Diabetes exerts an emerging burden in the region. Related complications such as diabetic retinopathy (DR) are expected to increase dramatically in prevalence. Challenged by shortage of ophthalmic services and poor accessibility to DR screening, the application of artificial intelligence (AI) using deep learning (DL) may be an alternative solution. This study aims to evaluate the real-world clinical effectiveness of a DL system in screening for DR and vision-threatening DR (VTDR) in the Zambian population with diabetes.

Methods : A total of 4513 images from 3101 eyes of 1578 Zambians with diabetes were prospectively recruited for this study. Two-field color 45-degree retinal fundus photographs were captured for each eye and graded according to International Classification of Diabetic Retinopathy Severity scale. Referable DR was defined as moderate non-proliferative DR (NPDR) or worse, diabetic macular edema and ungradable images; VTDR was designated as severe NPDR and proliferative DR. With reference to the retinal specialists’ grading, we calculated the area under the receiver operating curve (AUC), sensitivity and specificity for referable DR, and the detection rate of VTDR, using an Ensemble convolutional neural network.

Results : The prevalence of referable DR and VTDR in the Zambian population with diabetes were 23.1% and 6.4%, respectively. The AUC of the DL system for referable DR was 0.973 (95%CI, 0.968-0.977), with corresponding sensitivity of 91.35% (95%CI, 89.12-93.31) and specificity of 89.26% (95%CI, 88-90.48). VTDR detection rate was 93.91% (95%CI, 93.07-94.75).

Conclusions : The developed DL system shows clinically acceptable performance in detection of referable DR and VTDR for the Zambian population. This demonstrates the potential application to adopt such sophisticated cutting-edge AI technology for the underprivileged population in developing countries, to ultimately reduce the incidence of preventable blindness.

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

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