June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Non-invasive detection of retinal ischaemia using hyperspectral imaging
Author Affiliations & Notes
  • Peter van Wijngaarden
    Centre for Eye Research Australia Ltd, East Melbourne, Victoria, Australia
    Surgery, University of Melbourne, East Melbourne, Victoria, Australia
  • Darvy Dang
    Centre for Eye Research Australia Ltd, East Melbourne, Victoria, Australia
  • David Cordeiro Sousa
    Centre for Eye Research Australia Ltd, East Melbourne, Victoria, Australia
    Surgery, University of Melbourne, East Melbourne, Victoria, Australia
  • Amy C Cohn
    Centre for Eye Research Australia Ltd, East Melbourne, Victoria, Australia
  • Lyndell L Lim
    Centre for Eye Research Australia Ltd, East Melbourne, Victoria, Australia
    Surgery, University of Melbourne, East Melbourne, Victoria, Australia
  • Xavier Hadoux
    Centre for Eye Research Australia Ltd, East Melbourne, Victoria, Australia
  • Footnotes
    Commercial Relationships   Peter van Wijngaarden Roche, Code F (Financial Support), Bayer, Code F (Financial Support), Enlighten Imaging Pty Ltd, Code O (Owner), Enlighten Imaging Pty Ltd, Code P (Patent), Bayer, Code R (Recipient), Novartis, Code R (Recipient), Mylan, Code R (Recipient), Roche, Code R (Recipient); Darvy Dang None; David Sousa Zeiss, Code R (Recipient); Amy Cohn None; Lyndell Lim Roche, Code C (Consultant/Contractor), Novotech, Code C (Consultant/Contractor), Bayer, Code F (Financial Support), Roche, Code R (Recipient), Bayer, Code R (Recipient), Novartis, Code R (Recipient); Xavier Hadoux Enlighten Imaging Pty Ltd, Code O (Owner), Enlighten Imaging Pty Ltd, Code P (Patent)
  • Footnotes
    Support  Perpetual Impact Philanthropy Grant (H&L Hecht Trust) IPAP2022/126; Diabetes Australia Research Program Grant Y22G-VANP; Australian Vision Research Grant
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 3838. doi:
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    • Get Citation

      Peter van Wijngaarden, Darvy Dang, David Cordeiro Sousa, Amy C Cohn, Lyndell L Lim, Xavier Hadoux; Non-invasive detection of retinal ischaemia using hyperspectral imaging. Invest. Ophthalmol. Vis. Sci. 2023;64(8):3838.

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

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Abstract

Purpose : To determine whether hyperspectral retinal imaging can be used in combination with deep learning to detect retinal ischaemia.

Methods : We performed hyperspectral retinal imaging (Optina Diagnostics, Metabolic Hyperspectral Retinal Camera; wavelength range 450-900 nm) on 23 people with retinal ischaemia due to diabetic retinopathy or retinal vein occlusion and 32 people without ischaemia, prior to fundus fluorescein angiography (FFA) imaging. Avascular retinal areas on angiogram images were annotated by a retinal specialist using custom cross-modality annotation software. Machine learning was used to co-register annotated FFA and hyperspectral images. Data were randomly split into 45 images for training (>3.1M spectra) and 10 images for testing (>0.7M spectra). Spectral data (images acquired at 91 wavelengths per patient) were the input for a convolutional neural network comprised of a convolutional inception layer, one fully connected layer in the hidden layers and a classification layer. The network was trained and optimized for classification accuracy (ischaemic vs non-ischaemic pixels), without overfitting the training set. Restricting the input to spectral data alone limited the number of tuneable parameters (<16k), making model training more efficient and less prone to overfitting.

Results : Classification accuracy (per pixel) was 90.8% for the training set and 88.2% for the test set. The area under the receiver operating characteristic curve was 0.93 for training and 0.86 for testing (Figure 1), indicative of close correspondence with FFA for the detection of retinal ischaemia.

Conclusions : This study has demonstrated that retinal hyperspectral imaging can be used to identify areas of retinal ischaemia with close correspondence to FFA. Larger studies are required to validate these preliminary findings.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

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