Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.

Journal: Current rheumatology reports
Published Date: Aug 10, 2023

Abstract

PURPOSE OF REVIEW: Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research.

Authors

  • Liubov Arbeeva
    L. Arbeeva, MS, Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Mary C Minnig
    Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Katherine A Yates
    Harvard Medical School, Boston, Massachusetts.
  • Amanda E Nelson
    Department of Epidemiology, Gillings School of Global Public Health.

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