Machine learning and computer vision of bone microarchitecture can improve the fracture risk prediction provided by DXA and clinical risk factors
Machine learning and computer vision of bone microarchitecture can improve the fracture risk prediction provided by DXA and clinical risk factors
High-resolution peripheral quantitative computed tomography (HRpQCT) scanning provides such detailed, 3-dimensional reconstructions of the skeleton that the images have been called ‘a virtual bone biopsy’. Traditional analysis of the images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools such as FRAX. A computer vision approach, where the entire scan is ‘read’ by a computer algorithm to ascertain fracture risk, would be far simpler. Thus, we investigated whether a computer vision and machine learning technique could improve the current methods of assessing fracture risk.
Fuggle, N.R.
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Lu, Shengyu
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Breasail, Micheal O.
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Westbury, Leo
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Ward, Kate
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Dennison, Elaine
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Mahmoodi, Sasan
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Niranjan, Mahesan
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Cooper, Cyrus
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Fuggle, N.R.
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Lu, Shengyu
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Breasail, Micheal O.
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Westbury, Leo
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Ward, Kate
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Dennison, Elaine
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Mahmoodi, Sasan
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Niranjan, Mahesan
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Cooper, Cyrus
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Fuggle, N.R., Lu, Shengyu, Breasail, Micheal O., Westbury, Leo, Ward, Kate, Dennison, Elaine, Mahmoodi, Sasan, Niranjan, Mahesan and Cooper, Cyrus
(2022)
Machine learning and computer vision of bone microarchitecture can improve the fracture risk prediction provided by DXA and clinical risk factors.
Rheumatology, 61 (1), [OA22].
(doi:10.1093/rheumatology/keac132.022).
Abstract
High-resolution peripheral quantitative computed tomography (HRpQCT) scanning provides such detailed, 3-dimensional reconstructions of the skeleton that the images have been called ‘a virtual bone biopsy’. Traditional analysis of the images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools such as FRAX. A computer vision approach, where the entire scan is ‘read’ by a computer algorithm to ascertain fracture risk, would be far simpler. Thus, we investigated whether a computer vision and machine learning technique could improve the current methods of assessing fracture risk.
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e-pub ahead of print date: 23 April 2022
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Local EPrints ID: 475034
URI: http://eprints.soton.ac.uk/id/eprint/475034
ISSN: 1462-0324
PURE UUID: c1764b69-58ed-4146-97bc-acdcbd54ce8d
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Date deposited: 09 Mar 2023 17:33
Last modified: 18 Mar 2024 03:34
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Author:
N.R. Fuggle
Author:
Shengyu Lu
Author:
Micheal O. Breasail
Author:
Leo Westbury
Author:
Elaine Dennison
Author:
Sasan Mahmoodi
Author:
Mahesan Niranjan
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