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Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes

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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.

Recent Findings

AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery.

Summary

We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.

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Abbreviations

ACR :

American College of Rheumatology

AI :

artificial intelligence

AUC :

area under curve

BMI :

body mass index

CDW :

Clinical Data Warehouses

CNN :

convolutional neural network

DEC :

deep embedded clustering

DL :

deep learning

EHR :

electronic health record

GWAS :

genome-wide association studies

KLG :

Kellgren-Lawrence Grade

LR :

logistic regression

MFAC :

clustering with multiple factor analysis

ML :

machine learning

MRI :

magnetic resonance imaging

NSAID :

non-steroidal anti-inflammatory drugs

OA :

osteoarthritis

OAI :

Osteoarthritis Initiative

RCT :

randomized clinical trial

RF :

random forest

TJR :

total joint replacement

US :

ultrasound data

WOMAC :

Western Ontario and McMaster Universities Arthritis Index

XGB :

eXtreme Gradient Boosting

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Funding

Funding for this work was provided in part by NIH/NIAMS K24AR081368 and P30AR07250. The funders had no role in the writing or submission of the manuscript. Dr. Nelson also reports funding outside this work from NIH/NIAMS and the Rheumatology Research Foundation; she has received honoraria from Osteoarthritis and Cartilage and Nestle Health. The other authors report no competing interests.

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Authors and Affiliations

  1. Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, 3300 Doc J. Thurston Bldg, Campus Box #7280, Chapel Hill, NC, 27599-7280, USA

    Liubov Arbeeva, Katherine A. Yates & Amanda E. Nelson

  2. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Mary C. Minnig & Amanda E. Nelson

  3. Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Katherine A. Yates & Amanda E. Nelson

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  1. Liubov Arbeeva
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  2. Mary C. Minnig
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Correspondence to Amanda E. Nelson.

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Arbeeva, L., Minnig, M.C., Yates, K.A. et al. Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes. Curr Rheumatol Rep 25, 213–225 (2023). https://doi.org/10.1007/s11926-023-01114-9

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