Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (12:26:21 CEST)
How to cite:
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. Preprints2024, 2024090988. https://doi.org/10.20944/preprints202409.0988.v1
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. Preprints 2024, 2024090988. https://doi.org/10.20944/preprints202409.0988.v1
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. Preprints2024, 2024090988. https://doi.org/10.20944/preprints202409.0988.v1
APA Style
Mononen, M. E., Liukkonen, M. K., & Turunen, M. J. (2024). Machine Learning Model Trained with Finite Element Modeling Can Predict the Risk of Osteoarthritis: Data from the Osteoarthritis Initiative. Preprints. https://doi.org/10.20944/preprints202409.0988.v1
Chicago/Turabian Style
Mononen, M. 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" Preprints. https://doi.org/10.20944/preprints202409.0988.v1
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 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 performance (AUC = 0.73 - 0.74) at 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 knee for researchers or clinicians who have no former knowledge from FEA.
Keywords
knee; cartilage; finite element analysis; machine learning; osteoarthritis
Subject
Physical Sciences, Applied Physics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
