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. 2022 Dec 5;12:20998. doi: 10.1038/s41598-022-25467-w

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© The Author(s) 2022

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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ROC curve analysis of the deep learning algorithm for the differential diagnosis of LVH. The diagnostic accuracy of the deep learning algorithm was calculated using the AUC for the validation (A), and test sets (B). This figure was generated using R software v.4.1.1, R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.r-project.org/. AUC area under the ROC curve, CI confidence interval, ALCA light-chain cardiac amyloidosis, HCM hypertrophic cardiomyopathy, HHD hypertensive heart disease, ROC receiver operating characteristic curve.