July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Artificial Intelligence using Deep Learning System for Glaucoma Suspect Detection
Author Affiliations & Notes
  • Haslina Hamzah
    National University of Singapore, Singapore, Singapore
  • Gilbert Lim
    National University of Singapore, Singapore, Singapore
  • Quang duc nguyen
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
  • Baskaran Mani
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
    Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Graduate Medical School, National University of Singapore, Singapore, Singapore
  • Wynne Hsu
    National University of Singapore, Singapore, Singapore
  • Mong Li Lee
    National University of Singapore, Singapore, Singapore
  • Ching-Yu Cheng
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
    Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Graduate Medical School, National University of Singapore, Singapore, Singapore
  • Tien Y Wong
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
    Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Graduate Medical School, National University of Singapore, Singapore, Singapore
  • Daniel Ting
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
    Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Graduate Medical School, National University of Singapore, Singapore, Singapore
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 4074. doi:
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      Haslina Hamzah, Gilbert Lim, Quang duc nguyen, Baskaran Mani, Wynne Hsu, Mong Li Lee, Ching-Yu Cheng, Tien Y Wong, Daniel Ting; Artificial Intelligence using Deep Learning System for Glaucoma Suspect Detection. Invest. Ophthalmol. Vis. Sci. 2018;59(9):4074.

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

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Abstract

Purpose : Deep learning system (DLS) is a novel machine learning technology to screen for glaucoma suspect (GS), a major cause of blindness

Methods : We evaluated the diagnostic performance of a DLS for GS using multi-ethnic cohorts with 162,494 retinal images. We trained the DLS using 125,189 images among diabetes patients attending a Singapore national DR screening program (SiDRP), 3 population-based studies and 1 clinic-based cohorts and validated it using 7,326 images of patients from a population-based study with reference to glaucoma specialists and professional graders’ grading. We calculated area under curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Cohen’s Kappa, and repeatability (same image tested twice) of DLS in detecting GS (vertical cup to disc ratio of 0.8 or greater and other glaucomatous disc features).

Results : Using the clinical validation datasets (7,326 images, 5,090 eyes, 2,575 patients), the AUC was 0.949 (95%CI 0.942, 0.957). The sensitivity and specificity were 90.3% (88.5-91.9) and 83.0% (81.8, 84.2), respectively. The PPV and NPV were 62.4 (60.1, 64.7) and 96.5 (95.8, 97.1), respectively. Both DLS and human graders had moderate inter-rater agreement of 0.64 (0.61, 0.66), with a repeatability of 100%.

Conclusions : Deep learning technology had clinically acceptable diagnostic performance for GS screening in multi-ethnic populations, highlighting its significant public health potential worldwide.

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

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