August 2019
Volume 60, Issue 11
Open Access
ARVO Imaging in the Eye Conference Abstract  |   August 2019
Improvement and validation of high precision ocular oximetry using a convolutional neural network algorithm and a phantom eye
Author Affiliations & Notes
  • Damon T. DePaoli
    CERVO Brain Research Center, Quebec City, Quebec, Canada
    Université Laval, Qubeec, Quebec, Canada
  • Nicolas Lapointe
    Zilia, Quebec, Quebec, Canada
  • Prudencio Tossou
    Université Laval, Qubeec, Quebec, Canada
  • Joannie Desroches
    Zilia, Quebec, Quebec, Canada
  • Patrick Saugaveau
    Zilia, Quebec, Quebec, Canada
  • Daniel C. Côté
    CERVO Brain Research Center, Quebec City, Quebec, Canada
    Université Laval, Qubeec, Quebec, Canada
  • Dominic Sauvageau
    University of Alberta, Edmonton, Alberta, Canada
    Zilia, Quebec, Quebec, Canada
  • Footnotes
    Commercial Relationships   Damon DePaoli, Zilia (C); Nicolas Lapointe, Zilia (E); Prudencio Tossou, None; Joannie Desroches, Zilia (E); Patrick Saugaveau, Zilia (I); Daniel Côté, None; Dominic Sauvageau, Zilia (I)
  • Footnotes
    Support  Mitacs Accellerate
Investigative Ophthalmology & Visual Science August 2019, Vol.60, PB0101. doi:
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      Damon T. DePaoli, Nicolas Lapointe, Prudencio Tossou, Joannie Desroches, Patrick Saugaveau, Daniel C. Côté, Dominic Sauvageau; Improvement and validation of high precision ocular oximetry using a convolutional neural network algorithm and a phantom eye. Invest. Ophthalmol. Vis. Sci. 2019;60(11):PB0101.

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

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Abstract

Purpose : Retinal oximetry is a non-invasive imaging technology that enables the measurement of oxygen saturation (SO2) in the eye fundus. The goal of this research was to validate a convolutional neural network (CNN) algorithm designed to calculate and improve the precision of SO2 measurements from diffuse reflectance spectra (DRS) taken on the optic nerve head (ONH).

Methods : The ocular oximetry device developed by Zilia was used to acquire diffuse reflectance spectra (DRS) on several ONH-mimicking liquid optical phantoms (phantom eye). The oxygenation of the blood circulating in the phantom eyes was dynamically cycled from 100% to 0% oxygenation (using yeast and oxygen gas). SO2 measurements were made simultaneously with Zilia’s device and gold standard devices for comparison throughout the experiments. The phantom eyes were made to assess variations in blood volumes and scattering coefficients, corresponding to typical ranges observed for ONH optical properties. The procedure was then repeated with several cataract-simulating contact lenses integrated to the optical path to show robustness of oximetry measurements. Finally, we apply the algorithm to spectra acquired in vivo on subjects at baseline and hyperoxic conditions.

Results : We found good agreement in SO2 measurements between the results obtain with the Zilia device using the CNN algorithm and the gold standard references in all phantom eyes. We specifically show strong robustness in precision, even when all 3 of the experimental cataract-simulating lenses were used. This is significant since cataracts has traditionally plagued oximetry measurements. Lastly, we show that ocular oximetry measurements showed reliable increases in in vivo oxygenation, consistent with results from tissue oximetry, in test-subjects provided with 100% oxygen.

Conclusions : We present, here, further validation that the oximetry device and CNN algorithm developed to measure SO2 in the eye fundus produces reliable, precise measurements even under conditions where blood volume fractions vary, optical scattering changes, and cataract-simulating contact lenses are included. Furthermore, we show that the algorithm can be applied to in vivo measurements.

This abstract was presented at the 2019 ARVO Imaging in the Eye Conference, held in Vancouver, Canada, April 26-27, 2019.

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