Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Dec;19(6):699-709.
doi: 10.1007/s11914-021-00701-y.

Augmenting Osteoporosis Imaging with Machine Learning

Valentina Pedoia  1 , Francesco Caliva  2 , Galateia Kazakia  2 , Andrew J Burghardt  2 , Sharmila Majumdar  2
Affiliations
Review

Augmenting Osteoporosis Imaging with Machine Learning

Valentina Pedoia et al. Curr Osteoporos Rep. 2021 Dec.

Abstract

Purpose of review: In this paper, we discuss how recent advancements in image processing and machine learning (ML) are shaping a new and exciting era for the osteoporosis imaging field. With this paper, we want to give the reader a basic exposure to the ML concepts that are necessary to build effective solutions for image processing and interpretation, while presenting an overview of the state of the art in the application of machine learning techniques for the assessment of bone structure, osteoporosis diagnosis, fracture detection, and risk prediction.

Recent findings: ML effort in the osteoporosis imaging field is largely characterized by "low-cost" bone quality estimation and osteoporosis diagnosis, fracture detection, and risk prediction, but also automatized and standardized large-scale data analysis and data-driven imaging biomarker discovery. Our effort is not intended to be a systematic review, but an opportunity to review key studies in the recent osteoporosis imaging research landscape with the ultimate goal of discussing specific design choices, giving the reader pointers to possible solutions of regression, segmentation, and classification tasks as well as discussing common mistakes.

Keywords: Diagnosis; Fracture detection; Imaging; Machine learning; Osteoporosis; Risk prediction.

PubMed Disclaimer

References

Papers of particular interest, published recently, have been highlighted as: •• Of major importance
    1. van Oostwaard M Osteoporosis and the nature of fragility fracture: an overview. In: Hertz K, Santy-Tomlinson J, editors. Fragility fracture nursing: holistic care and management of the orthogeriatric patient. Cham (CH)2018. p. 1-13.
    1. Lewiecki EM, Lane NE. Common mistakes in the clinical use of bone mineral density testing. Nat Clin Pract Rheumatol. 2008;4(12):667–74. https://doi.org/10.1038/ncprheum0928 . - DOI - PubMed - PMC
    1. Compston J, Cooper A, Cooper C, Gittoes N, Gregson C, Harvey N, Hope S, Kanis JA, McCloskey EV, Poole KES, Reid DM, Selby P, Thompson F, Thurston A, Vine N, National Osteoporosis Guideline G. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2017;12(1):43. https://doi.org/10.1007/s11657-017-0324-5 . - DOI - PubMed - PMC
    1. Han W, Qin L, Bay C, Chen X, Yu KH, Miskin N, Li A, Xu X, Young G. Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas. AJNR Am J Neuroradiol. 2020;41(1):40–8. https://doi.org/10.3174/ajnr.A6365 . - DOI - PubMed - PMC
    1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539 . - DOI - PubMed

LinkOut - more resources