Document Type : SYSTEMATIC REVIEW
Authors
1
1 Department of Health Sciences Research, Mayo Clinic, 200 First St SW Rochester, Rochester, Minnesota, USA 2 Department of Orthopedics, Mayo Clinic, 200 First St SW Rochester, Rochester, Minnesota, USA
2
Department of Health Sciences Research, Mayo Clinic, 200 First St SW Rochester, Rochester, Minnesota, USA
3
Department of Orthopedics, Mayo Clinic, 200 First St SW Rochester, Rochester, Minnesota, USA
Abstract
Background: Knee osteoarthritis (OA) is a prevalent joint disease. Clinical prediction models consider a wide range
of risk factors for knee OA. This review aimed to evaluate published prediction models for knee OA and identify
opportunities for future model development.
Methods: We searched Scopus, PubMed, and Google Scholar using the terms knee osteoarthritis, prediction model,
deep learning, and machine learning. All the identified articles were reviewed by one of the researchers and we recorded
information on methodological characteristics and findings. We only included articles that were published after 2000
and reported a knee OA incidence or progression prediction model.
Results: We identified 26 models of which 16 employed traditional regression-based models and 10 machine learning
(ML) models. Four traditional and five ML models relied on data from the Osteoarthritis Initiative. There was significant
variation in the number and type of risk factors. The median sample size for traditional and ML models was 780
and 295, respectively. The reported Area Under the Curve (AUC) ranged between 0.6 and 1.0. Regarding external
validation, 6 of the 16 traditional models and only 1 of the 10 ML models validated their results in an external data set.
Conclusion: Diverse use of knee OA risk factors, small, non-representative cohorts, and use of magnetic resonance
imaging which is not a routine evaluation tool of knee OA in daily clinical practice are some of the main limitations of
current knee OA prediction models.
Level of evidence: III
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