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

Abstract 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...

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Autores principales:	Shotaro Asano, Ryo Asaoka, Takehiro Yamashita, Shuichiro Aoki, Masato Matsuura, Yuri Fujino, Hiroshi Murata, Shunsuke Nakakura, Yoshitaka Nakao, Yoshiaki Kiuchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3437b8342d7f412c9c26967ccf4a2816
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spelling	oai:doaj.org-article:3437b8342d7f412c9c26967ccf4a28162021-12-02T18:27:50ZVisualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle10.1038/s41598-020-63601-82045-2322https://doaj.org/article/3437b8342d7f412c9c26967ccf4a28162020-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-63601-8https://doaj.org/toc/2045-2322Abstract 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.Shotaro AsanoRyo AsaokaTakehiro YamashitaShuichiro AokiMasato MatsuuraYuri FujinoHiroshi MurataShunsuke NakakuraYoshitaka NakaoYoshiaki KiuchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shotaro Asano Ryo Asaoka Takehiro Yamashita Shuichiro Aoki Masato Matsuura Yuri Fujino Hiroshi Murata Shunsuke Nakakura Yoshitaka Nakao Yoshiaki Kiuchi Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle
description	Abstract 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.
format	article
author	Shotaro Asano Ryo Asaoka Takehiro Yamashita Shuichiro Aoki Masato Matsuura Yuri Fujino Hiroshi Murata Shunsuke Nakakura Yoshitaka Nakao Yoshiaki Kiuchi
author_facet	Shotaro Asano Ryo Asaoka Takehiro Yamashita Shuichiro Aoki Masato Matsuura Yuri Fujino Hiroshi Murata Shunsuke Nakakura Yoshitaka Nakao Yoshiaki Kiuchi
author_sort	Shotaro Asano
title	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_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_sort	visualizing the dynamic change of ocular response analyzer waveform using variational autoencoder in association with the peripapillary retinal arteries angle
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/3437b8342d7f412c9c26967ccf4a2816
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Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle

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