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Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle

The aim of the current study is to identify possible new Ocular Response Analyzer (ORA) waveform parameters related to changes of retinal structure/deformation, as measured by the peripapillary retinal arteries angle (PRAA), using a generative deep learning method of variational autoencoder (VAE). F...

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Autores principales: Asano, Shotaro, Asaoka, Ryo, Yamashita, Takehiro, Aoki, Shuichiro, Matsuura, Masato, Fujino, Yuri, Murata, Hiroshi, Nakakura, Shunsuke, Nakao, Yoshitaka, Kiuchi, Yoshiaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170838/
https://www.ncbi.nlm.nih.gov/pubmed/32313133
http://dx.doi.org/10.1038/s41598-020-63601-8
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author Asano, Shotaro
Asaoka, Ryo
Yamashita, Takehiro
Aoki, Shuichiro
Matsuura, Masato
Fujino, Yuri
Murata, Hiroshi
Nakakura, Shunsuke
Nakao, Yoshitaka
Kiuchi, Yoshiaki
author_facet Asano, Shotaro
Asaoka, Ryo
Yamashita, Takehiro
Aoki, Shuichiro
Matsuura, Masato
Fujino, Yuri
Murata, Hiroshi
Nakakura, Shunsuke
Nakao, Yoshitaka
Kiuchi, Yoshiaki
author_sort Asano, Shotaro
collection PubMed
description The aim of the current study is to identify possible new Ocular Response Analyzer (ORA) waveform parameters related to changes of retinal structure/deformation, as measured by the peripapillary retinal arteries angle (PRAA), using a generative deep learning method of variational autoencoder (VAE). Fifty-four eyes of 52 subjects were enrolled. The PRAA was calculated from fundus photographs and was used to train a VAE model. By analyzing the ORA waveform reconstructed (noise filtered) using VAE, a novel ORA waveform parameter (Monot1-2), was introduced, representing the change in monotonicity between the first and second applanation peak of the waveform. The variables mostly related to the PRAA were identified from a set of 41 variables including age, axial length (AL), keratometry, ORA corneal hysteresis, ORA corneal resistant factor, 35 well established ORA waveform parameters, and Monot1-2, using a model selection method based on the second-order bias-corrected Akaike information criterion. The optimal model for PRAA was the AL and six ORA waveform parameters, including Monot1-2. This optimal model was significantly better than the model without Monot1-2 (p = 0.0031, ANOVA). The current study suggested the value of a generative deep learning approach in discovering new useful parameters that may have clinical relevance.
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spelling pubmed-71708382020-04-23 Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle Asano, Shotaro Asaoka, Ryo Yamashita, Takehiro Aoki, Shuichiro Matsuura, Masato Fujino, Yuri Murata, Hiroshi Nakakura, Shunsuke Nakao, Yoshitaka Kiuchi, Yoshiaki Sci Rep Article The aim of the current study is to identify possible new Ocular Response Analyzer (ORA) waveform parameters related to changes of retinal structure/deformation, as measured by the peripapillary retinal arteries angle (PRAA), using a generative deep learning method of variational autoencoder (VAE). Fifty-four eyes of 52 subjects were enrolled. The PRAA was calculated from fundus photographs and was used to train a VAE model. By analyzing the ORA waveform reconstructed (noise filtered) using VAE, a novel ORA waveform parameter (Monot1-2), was introduced, representing the change in monotonicity between the first and second applanation peak of the waveform. The variables mostly related to the PRAA were identified from a set of 41 variables including age, axial length (AL), keratometry, ORA corneal hysteresis, ORA corneal resistant factor, 35 well established ORA waveform parameters, and Monot1-2, using a model selection method based on the second-order bias-corrected Akaike information criterion. The optimal model for PRAA was the AL and six ORA waveform parameters, including Monot1-2. This optimal model was significantly better than the model without Monot1-2 (p = 0.0031, ANOVA). The current study suggested the value of a generative deep learning approach in discovering new useful parameters that may have clinical relevance. Nature Publishing Group UK 2020-04-20 /pmc/articles/PMC7170838/ /pubmed/32313133 http://dx.doi.org/10.1038/s41598-020-63601-8 Text en © The Author(s) 2020 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/.
spellingShingle Article
Asano, Shotaro
Asaoka, Ryo
Yamashita, Takehiro
Aoki, Shuichiro
Matsuura, Masato
Fujino, Yuri
Murata, Hiroshi
Nakakura, Shunsuke
Nakao, Yoshitaka
Kiuchi, Yoshiaki
Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle
title Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle
title_full Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle
title_fullStr Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle
title_full_unstemmed Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle
title_short Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle
title_sort visualizing the dynamic change of ocular response analyzer waveform using variational autoencoder in association with the peripapillary retinal arteries angle
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170838/
https://www.ncbi.nlm.nih.gov/pubmed/32313133
http://dx.doi.org/10.1038/s41598-020-63601-8
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