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 language | English |
|---|---|
| Article number | 117071 |
| Journal | Bone |
| Volume | 182 |
| Early online date | 15 Mar 2024 |
| DOIs |
|
| Publication status | Published - 1 May 2024 |
Bibliographical note
Publisher Copyright:© 2024 The Author(s)
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Dive into the research topics of '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))'. Together they form a unique fingerprint.Research output
- 1 Article (Academic Journal)
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Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors
Lu, S., Fuggle, N. R., Westbury, L. D., Ó Breasail, M., Bevilacqua, G., Ward, K. A., Dennison, E. M., Mahmoodi, S., Niranjan, M. & Cooper, C., 1 Mar 2023, In: Bone. 168, 7 p., 116653.Research output: Contribution to journal › Article (Academic Journal) › peer-review
Open AccessFile19 Citations (Scopus)93 Downloads (Pure)
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