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Corrigendum to “Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors” [Bone. 2023 Mar:168:116653] (Bone (2023) 168, (S8756328222003301), (10.1016/j.bone.2022.116653))

Shengyu Lu, Nicholas R. Fuggle, Leo D. Westbury, Mícheál Ó Breasail, Gregorio Bevilacqua, Kate A. Ward, Elaine M. Dennison, Sasan Mahmoodi, Mahesan Niranjan, Cyrus Cooper*

*Corresponding author for this work

Research output: Contribution to journalComment/debate (Academic Journal)

Abstract

The authors regret the omission of the following italicised text within the methods section of the above paper: A sample of 22 consecutive slices was selected for each HR-pQCT image. There were fewer participants with previous fractures compared to those without. Therefore, an oversampling strategy was used for individuals with previous fractures [33], such that multiple samples were taken from the scans of those with previous fractures. This was performed assuming intra-scan homogeneity and to provide balance to the machine learning dataset. The authors would like to apologise for any inconvenience caused.

Original languageEnglish
Article number117071
JournalBone
Volume182
Early online date15 Mar 2024
DOIs
Publication statusPublished - 1 May 2024

Bibliographical note

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© 2024 The Author(s)

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