Augmenting Osteoporosis Imaging with Machine Learning.

Journal: Current osteoporosis reports
Published Date: Dec 1, 2021

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.

Authors

  • Valentina Pedoia
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • Francesco Calivá
    School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK.
  • Galateia Kazakia
    Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 1700 Fourth Street, Suite 201, QB3 Building, San Francisco, CA, 94158, USA.
  • Andrew J Burghardt
    Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), 1700 Fourth Street, Suite 201, QB3 Building, San Francisco, CA, 94158, USA.
  • Sharmila Majumdar
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA.

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