Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder

In this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry...

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Bibliographic Details
Published inScientific reports Vol. 9; no. 1; pp. 18113 - 12
Main Authors Berchuck, Samuel I., Mukherjee, Sayan, Medeiros, Felipe A.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 02.12.2019
Nature Publishing Group
Subjects
639/705/531
692/308/409
Aged
Glaucoma
Glaucoma - diagnosis
Glaucoma - physiopathology
Humanities and Social Sciences
Humans
Image Processing, Computer-Assisted
Middle Aged
Models, Biological
multidisciplinary
Science
Science (multidisciplinary)
Visual field
Visual Field Tests - methods
Visual Fields
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Summary:In this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4 th , 6 th and 8 th visits from the first three (e.g., visit eight: VAE8: 5.14 dB vs. PW: 8.07 dB; P < 0.001). A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-54653-6