July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
A Multi-device, Multi-ethnicity Deep Learning Algorithm to Detect Glaucoma from A Single Optical Coherence Tomography Scan of the Optic Nerve Head
Author Affiliations & Notes
  • Liang Zhang
    Biomedical Engineering, National University of Singapore, Singapore, Singapore
  • Sripad Krishna Devalla
    Biomedical Engineering, National University of Singapore, Singapore, Singapore
  • Ching-Yu Cheng
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Dan Milea
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Monisha Esther Nongpiur
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Baskaran Mani
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Mitchell Lawlor
    Save Sight Institute, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
  • Mukharram Bikbov
    Ufa Eye Research Institute, Ufa, Republic of Bashkortostan, Russian Federation
  • Carol Yim-lui Cheung
    Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
  • Ya Xing Wang
    Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, BeiJing, China
  • Xiulan Zhang
    Zhongshan Ophthalmic Center, ZhongShan, China
  • Aiste Kadziauskiene
    Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
  • Tin Aung
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Alexandre Thiery
    Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
  • Michael J A Girard
    Biomedical Engineering, National University of Singapore, Singapore, Singapore
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  • Footnotes
    Commercial Relationships   Liang Zhang, None; Sripad Krishna Devalla, None; Ching-Yu Cheng, None; Dan Milea, None; Monisha Nongpiur, None; Baskaran Mani, None; Mitchell Lawlor, None; Mukharram Bikbov, None; Carol Cheung, None; Ya Xing Wang, None; Xiulan Zhang, None; Aiste Kadziauskiene, None; Tin Aung, None; Alexandre Thiery, Abyss Processing Pte Ltd (S); Michael Girard, Abyss Processing Pte Ltd (S)
  • Footnotes
    Support  Singapore Ministry of Education Tier 1 and Tier 2
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2207. doi:
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      Liang Zhang, Sripad Krishna Devalla, Ching-Yu Cheng, Dan Milea, Monisha Esther Nongpiur, Baskaran Mani, Mitchell Lawlor, Mukharram Bikbov, Carol Yim-lui Cheung, Ya Xing Wang, Xiulan Zhang, Aiste Kadziauskiene, Tin Aung, Alexandre Thiery, Michael J A Girard; A Multi-device, Multi-ethnicity Deep Learning Algorithm to Detect Glaucoma from A Single Optical Coherence Tomography Scan of the Optic Nerve Head. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2207.

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

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Abstract

Purpose : To develop a multi-ethnicity and multi-device deep learning algorithm that can detect glaucoma from a single 3D optical coherence tomography (OCT) scan of the optic nerve head (ONH).

Methods : Diagonal & Raster OCT scans were acquired from 4 OCT machines (Spectralis, Cirrus, Atlantis, Nidek RS-3000) through the center of the ONH for subjects from Australia (Sydney), China (Hong Kong, Bei Jing and Zhong Shan), Lithuania (Vilnius), Russia (Ufa) and Singapore. For consistency, each OCT scan was then converted to a radial scan with 6 B-scans passing through the center of Bruch’s membrane opening. In total, 55,902 B-scans were used from 5,747 normal subjects and 39,096 B-scans from 2,363 glaucoma subjects (Figure 1.a). For each OCT device, all scans were balanced through up-sampling, and split into a training set (70% of scans), a validation set (15%) and an independent testing set (15%). To classify glaucoma from non-glaucoma subjects, we developed a custom convolutional neural network (deep learning) that exploited 3D structural information about neural and connective tissues of the ONH. Extensive online data augmentations (e.g. translation, rotation, flipping, and noise addition) were performed to improve accuracy. We calculated the area under the receiver operating characteristic curve (AUC) to assess whether our deep learning algorithm was able to differentiate glaucoma from non-glaucoma eyes.

Results : Our deep learning algorithm was able to accurately classify glaucoma from non-glaucoma with an AUC of 0.90 (Figure 1.b) on the independent testing set.

Conclusions : We propose a novel deep learning approach that provides a glaucoma diagnosis from a single OCT scan, using multi-device and multi-ethnicity datasets. Our approach may simplify glaucoma diagnosis as it is fast (diagnosis is computed in less than 1 second), and it only requires 1 clinical test (i.e. OCT imaging of the ONH).

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

 

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