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. 2023 Oct 25;13:18304. doi: 10.1038/s41598-023-43104-y

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© The Author(s) 2023

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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(A) Workflow of this study. Input data extracted from OCT images are used to predict VF indexes (MD, PSD, VFI) through a DNN model. Detailed structure of dashed line box with a red star is described in (B). (B) Detailed structure of the DNN model. Each number above each box represents the number of nodes in the prior layer. The number below each box means the number of nodes in the present layer. OCT = optical coherence tomography; MD = mean deviation; PSD = pattern standard deviation; VFI = visual field index; DNN = deep neural network.