June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Assessing Repeatability of Deep-Learning Based Estimation of Visual Function Test Parameters from OCT Volumes
Author Affiliations & Notes
  • Bhavna Josephine Antony
    IBM Research Australia, Southbank, Victoria, Australia
  • Hiroshi Ishikawa
    Department of Ophthalmology, NYU Langone Health, New York, New York, United States
  • Gadi Wollstein
    Department of Ophthalmology, NYU Langone Health, New York, New York, United States
  • Joel S Schuman
    Department of Ophthalmology, NYU Langone Health, New York, New York, United States
  • Rahil Garnavi
    IBM Research Australia, Southbank, Victoria, Australia
  • Footnotes
    Commercial Relationships   Bhavna Antony, IBM Research (E); Hiroshi Ishikawa, None; Gadi Wollstein, None; Joel Schuman, Zeiss (P); Rahil Garnavi, IBM Research (E)
  • Footnotes
    Support  R01-EY013178, R01-EY030929, Unrestricted grant by Research to Prevent Blindness
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 1778. doi:
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      Bhavna Josephine Antony, Hiroshi Ishikawa, Gadi Wollstein, Joel S Schuman, Rahil Garnavi; Assessing Repeatability of Deep-Learning Based Estimation of Visual Function Test Parameters from OCT Volumes. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1778.

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

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Abstract

Purpose : To assess the repeatability of estimates of mean deviation (MD) and visual field index (VFI) obtained from an automated deep-learning approach that analysed raw OCT volumes.

Methods : OCT scans were acquired from both eyes of 138 healthy, 743 glaucoma suspects and 941 glaucoma patients (Cirrus HD-OCT scanner, 200x200 ONH Cubes, Zeiss, Dublin CA). The scans were acquired at multiple visits, with two or more scans acquired at each visit. Scans with signal strength < 7 were discarded, giving us a total of 19,208 OCT scans. A subset of 5207 eyes (total of 10,414 scans) had repeat scans of that met the inclusion criteria. 24-2 Humphrey visual field (VF) tests were administered at each visit. A single convolutional neural network was trained to estimate the MD and VFI (dual outputs) from downsampled OCT volumes (50x50x128 voxels). The network consisted of 5 convolutional layers, followed by a global average pooling layer and dual outputs to enable the simultaneous estimation of MD and VFI. A mean squared error loss was used to train the network using an Adam optimiser over a total of 200 epochs. A 10-fold cross-validation scheme was used, where the dataset was divided into 10 non-overlapping folds (~182 subjects per fold) – trained on 8-folds, validated on one and tested on one. Each subject was limited to a unique fold. The performance of the method was assessed by computing the median error and interquartile range. The repeatability was assessed using a set of 5207 OCT scans that had repeats available.

Results : The median absolute error (Q1, Q3) for the estimates of MD and VFI were 1.66 (0.79, 2.99) dB and 3.01 (1.48, 6.63) %, respectively. In the reproducibility test, the Pearson’s correlation coefficient was 0.91 (CI: [0.91, 0.92]) and 0.91 (CI: [0.90, 0.92]), for MD and VFI, respectively. The median absolute difference between the repeated estimates for MD and VFI were 0.53 (0.21, 0.51) dB and 1.17 (0.45, 1.14)%, respectively.

Conclusions : The deep-learning based approach for estimating visual field test parameters shows repeatability better than expected test-to-test variability.

This is a 2021 ARVO Annual Meeting abstract.

 

Scatter plot of VF parameter estimates in the repeat scans. Signal strength of the first scan is also shown.

Scatter plot of VF parameter estimates in the repeat scans. Signal strength of the first scan is also shown.

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