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. 2023 Jan 11;13:558. doi: 10.1038/s41598-023-27783-1

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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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Scatter plots showing the associations between the visual field mean deviation (VFMD) and measured retinal nerve fiber layer (RNFL) thickness for (A) Asian and (D) Caucasian test datasets. (B) and (E) show the associations between VFMD, and the probability score generated by a machine learning (ML) model using measured RNFL thickness, where a lower score indicates greater severity of glaucoma. (C) and (F) show the associations between VFMD, and the probability score generated by a ML model using compensated RNFL thickness, where a higher score indicates greater severity of glaucoma. Spearman’s correlation coefficient ρ and LOWESS curves are indicated for the corresponding variables in each plot.