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. 2022 Mar 10;14:41. doi: 10.1186/s13195-022-00982-0

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© The Author(s) 2022

Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Steps to account for ocular factors from circumpapillary retinal nerve fiber layer (cpRNFL) measurement. A, B Capture the optical coherence tomography (OCT) scan protocols using Cirrus (Zeiss) system, one centered in the macula and the other centered in the optic disc. C–E Extract the cpRNFL measurements using Cirrus Review software and the individual macular layers using Iowa Reference Algorithms version 3.8.0 of the OCT layer segmentation program. F Register and stitch the macular and optic disc images. G Segment the retinal vessels to obtain the vessel tree. H Extract the optic disc and fovea features. I Calculate the cpRNFL retinal thickness, using a multi-regression compensation model. J Finally, the ideal model would reduce the variability of cpRNFL thickness measurements and/or improve disease detection