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Hsin-Hao Yu, Bhavna Josephine Antony, Hiroshi Ishikawa, Gadi Wollstein, Joel S Schuman, Rahil Garnavi; Estimating visual field progression rates of glaucoma patients using estimates derived from OCT scans. Invest. Ophthalmol. Vis. Sci. 2020;61(7):875.
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© ARVO (1962-2015); The Authors (2016-present)
To develop a method for monitoring the functional deterioration of glaucoma patents using structural surrogates, we used machine learning algorithms to estimate visual field index (VFI) from OCT scans, and evaluated the accuracy of the progression rates calculated from the estimated VFI.
Macular and ONH SDOCT scans (Cirrus HD-OCT, Zeiss, Dublin, CA; 200x200x1024 samplings over 6x6x2mm, downsampled to 64x64x128 voxels) were acquired from both eyes of 1,678 healthy participants, glaucoma suspects, and glaucoma patients over multiple visits (range: 1-14, median=3), forming a dataset of 10,172 pairs of macular+ONH scans. Automated perimetry (Humphrey visual field, SITA 24-2) tests were administered at each visit. Two models were trained to estimate the measured VFI from a pair of macular and ONH scans: the first ("classic model") was a non-linear regression model (multi-layer perceptron) based on 47 thickness measures of retinal layers, while the other (“CNN”) was a 5-layer convolutional neural network, trained to learn 3D features in the OCT scans. For both models, MSE was minimized in 5-fold cross-validation, using 80%:10%:10% of the dataset as training, validation and test sets. Data from the same participant were not split across the three sets. For data in the test sets, VFI's for eyes with more than N=3,4,5 visits were estimated for individual visits, and the slopes were calculated using linear regression across N consecutive visits. Median absolute error (MAE) was used to quantify estimation accuracy.
For estimating VFI at single visits, the CNN achieved significant lower MAE (2.6±0.28; mean and s.d.) than the classic model (2.9±0.45). For estimating slopes across 5 visits, the MAE of the CNN (0.73±0.12/year) was also lower than the classic model (0.82±0.23/year). The errors depended on the measured VFI of the first visit, and on the true slope (Fig. 1). Increasing the number of visits decreased the errors (N=3...6, MAE=1.38/yr, 0.99/yr, 0.73/yr, and 0.63yr)
The feature-agnostic CNN was better at estimating VFI and visual field progression rates than the regression method based on thickness measures. Structure-to-function estimation using neural networks is a promising method for monitoring the visual functions of glaucoma patients
This is a 2020 ARVO Annual Meeting abstract.
The absolute error of the slope estimation was dependent on the VFI of the first visit and on the true slope.
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