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Open AccessArticle
Machine Learning Model Trained with Finite Element Modeling Can Predict the Risk of Osteoarthritis: Data from the Osteoarthritis Initiative
by
Mika E. Mononen
Mika E. Mononen 1,*,
Mimmi K. Liukkonen
Mimmi K. Liukkonen 2 and
Mikael J. Turunen
Mikael J. Turunen 1,3
1
Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, Finland
2
Department of Clinical Radiology, Kuopio University Hospital, The Wellbeing Services County of North Savo, 70200 Kuopio, Finland
3
Science Service Center, Kuopio University Hospital, The Wellbeing Services County of North Savo, 70200 Kuopio, Finland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(20), 9538; https://doi.org/10.3390/app14209538 (registering DOI)
Submission received: 11 September 2024
/
Revised: 11 October 2024
/
Accepted: 14 October 2024
/
Published: 18 October 2024
Abstract
Objective: Despite long simulation times, recently developed finite element analysis (FEA) models of knee joints have demonstrated their suitability for predicting individual risk of onset and progression of knee osteoarthritis. Therefore, the objective of this study was to assess the feasibility of machine learning (ML) to replicate outcomes obtained from FEA when simulating mechanical responses and predicting cartilage degeneration within the knee joint.Design: Two ML models based on the Gaussian Process Regression (GPR) algorithms were developed. The first model (GPR1) utilized age, weight, and anatomical joint dimensions as predictor variables to predict tissue mechanical responses and cartilage degeneration based on FEA data. The second model (GPR2) utilized age, weight, height, and gender to predict anatomical joint dimensions, which were then used as inputs in the GPR1 model. Finally, the GPR1 and combined GPR1+GPR2 models were used to investigate the importance of clinical imaging when making personalized predictions for knees from healthy subjects with no history of knee injuries.Results: In the GPR1 model, R2 of 0.9 was exceeded for most of the predicted mechanical parameters. The GPR2 model was able to predict knee shape with R2 of 0.67–0.9. Both GPR1 and combined GPR1+GPR2 models offered equally good performances (AUC = 0.73–0.74) in classifying patients at high risk for the onset and development of knee osteoarthritis.Conclusions: In the future, real-time and easy-to-use GPR models may provide a rapid technology to evaluate mechanical responses within the knee for researchers or clinicians who have no former knowledge of FEA.
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MDPI and ACS Style
Mononen, M.E.; Liukkonen, M.K.; Turunen, M.J.
Machine Learning Model Trained with Finite Element Modeling Can Predict the Risk of Osteoarthritis: Data from the Osteoarthritis Initiative. Appl. Sci. 2024, 14, 9538.
https://doi.org/10.3390/app14209538
AMA Style
Mononen ME, Liukkonen MK, Turunen MJ.
Machine Learning Model Trained with Finite Element Modeling Can Predict the Risk of Osteoarthritis: Data from the Osteoarthritis Initiative. Applied Sciences. 2024; 14(20):9538.
https://doi.org/10.3390/app14209538
Chicago/Turabian Style
Mononen, Mika E., Mimmi K. Liukkonen, and Mikael J. Turunen.
2024. "Machine Learning Model Trained with Finite Element Modeling Can Predict the Risk of Osteoarthritis: Data from the Osteoarthritis Initiative" Applied Sciences 14, no. 20: 9538.
https://doi.org/10.3390/app14209538
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