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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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          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.

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            Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs

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              The Insignificance of Statistical Significance Testing

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                Author and article information

                Contributors
                rasaoka-tky@umin.ac.jp
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 April 2020
                20 April 2020
                2020
                : 10
                : 6592
                Affiliations
                [1 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, Department of Ophthalmology, , Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, ; Tokyo, 113-8655 Japan
                [2 ]Seirei General Hospital, Shizuoka, 430-8558 Japan
                [3 ]ISNI 0000 0004 0373 7825, GRID grid.443623.4, Seirei Christopher University, ; Shizuoka, 433-8558 Japan
                [4 ]ISNI 0000 0001 1167 1801, GRID grid.258333.c, Kagoshima University Graduate School of Medical and Dental Sciences, ; Kagoshima, 890-0075 Japan
                [5 ]ISNI 0000 0000 9206 2938, GRID grid.410786.c, Department of Ophthalmology, , Graduate School of Medical Sciences, Kitasato University, ; Kanagawa, 252-0374 Japan
                [6 ]Department of Ophthalmology, Saneikai Tsukazaki Hospital, Hyogo, 671-1227 Japan
                [7 ]ISNI 0000 0000 8711 3200, GRID grid.257022.0, Department of Ophthalmology and Visual Science, , Hiroshima University, ; Hiroshima, 739-8511 Japan
                Author information
                http://orcid.org/0000-0001-7801-2317
                http://orcid.org/0000-0001-6082-0738
                http://orcid.org/0000-0003-1915-1864
                Article
                63601
                10.1038/s41598-020-63601-8
                7170838
                32313133
                339edb6c-717a-49f8-9f4e-896804eb8186
                © 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/.

                History
                : 24 September 2019
                : 31 March 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001700, Ministry of Education, Culture, Sports, Science and Technology (MEXT);
                Award ID: 17K11418
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                predictive markers,refractive errors
                Uncategorized
                predictive markers, refractive errors

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