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. 2023 May 16;3:67. doi: 10.1038/s43856-023-00299-5

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© The Author(s) 2023, corrected publication 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 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/.

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Fig. 4 — Distribution of aGVHD prediction scores calculated by the convolutional neural network (CNN) model are displayed among patients in the test cohort (N = 3753). A For grade II–IV aGVHD. Low (scores, 0.136–0.209), n = 375 (10.0%); Int (scores, 0.209–0.770), n = 3003 (80.0%); and High (scores, 0.770–0.894), n = 375 (10.0%). B For grade III–IV aGVHD. Low (scores, 0.080–0.138), n = 375 (10.0%); Int (scores, 0.138–0.840), n = 3003 (80.0%); and High (scores, 0840–0.951), n = 375 (10.0%). Higher scores indicate a higher risk of developing aGVHD. The data used to plot the graphs is in Supplemental Data 7.