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. 2019 Dec 2;9:18113. doi: 10.1038/s41598-019-54653-6

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Visualizing the latent space of the VAE in two dimensions using a linear model. (A) The top frame shows predicted trajectories through the latent space, presented as arrows, are presented for example patients of each disease status. Underlaying the trajectories are the observed longitudinal follow-up for each patient using muted colors. The healthy visual field in latent space is a blue square near the origin. (B) The bottom left frame shows the longitudinal latent features from the first dimension for the example patients. OLS regression lines are presented, which indicate rates of change in each latent dimension. (C) The second dimension is shown in the bottom right.