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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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1 Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Department of Ophthalmology, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X)
2 Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Department of Ophthalmology, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X); Seirei General Hospital, Shizuoka, Japan (GRID:grid.26999.3d); Seirei Christopher University, Shizuoka, Japan (GRID:grid.443623.4) (ISNI:0000 0004 0373 7825)
3 Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan (GRID:grid.258333.c) (ISNI:0000 0001 1167 1801)
4 Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Department of Ophthalmology, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X); Graduate School of Medical Sciences, Kitasato University, Department of Ophthalmology, Kanagawa, Japan (GRID:grid.410786.c) (ISNI:0000 0000 9206 2938)
5 Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Department of Ophthalmology, Tokyo, Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X); Seirei General Hospital, Shizuoka, Japan (GRID:grid.26999.3d); Graduate School of Medical Sciences, Kitasato University, Department of Ophthalmology, Kanagawa, Japan (GRID:grid.410786.c) (ISNI:0000 0000 9206 2938)
6 Saneikai Tsukazaki Hospital, Department of Ophthalmology, Hyogo, Japan (GRID:grid.26999.3d)
7 Hiroshima University, Department of Ophthalmology and Visual Science, Hiroshima, Japan (GRID:grid.257022.0) (ISNI:0000 0000 8711 3200)