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Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty

Author

Listed:
  • Jun Young Kim

    (Department of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Muhammad Sohail

    (Department of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Heung Soo Kim

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

Abstract

Total knee arthroplasty (TKA) is a surgical technique to replace damaged knee joints with artificial implants. Recently, the imageless TKA has brought a revolutionary improvement to the accuracy of implant placement and ease of surgical process. Based on key anatomical points on the knee, the software guides the surgeon during the TKA procedure. However, the number of revision surgeries is increasing due to malalignment caused by registration error, resulting in imbalanced contact stresses that lead to failure of the TKA. Conventional stress analysis methods involve time-consuming and computationally demanding finite element analysis (FEA). In this work, a machine-learning-based approach estimates the contact pressure on the TKA implants. The machine learning regression model has been trained using FEA data. The optimal preprocessing technique was confirmed by the data without preprocessing, data divided by model size, and data divided by model size and optimal angle. Extreme gradient boosting, random forest, and extra trees regression models were trained to determine the optimal approach. The proposed method estimates the contact stress instantly within 10 percent of the maximum error. This has resulted in a significant reduction in computational costs. The efficiency and reliability of the proposed work have been validated against the published literature.

Suggested Citation

  • Jun Young Kim & Muhammad Sohail & Heung Soo Kim, 2023. "Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty," Mathematics, MDPI, vol. 11(16), pages 1-15, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3527-:d:1217807
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    References listed on IDEAS

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    1. Jérôme Allyn & Nicolas Allou & Pascal Augustin & Ivan Philip & Olivier Martinet & Myriem Belghiti & Sophie Provenchere & Philippe Montravers & Cyril Ferdynus, 2017. "A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-12, January.
    2. Muhammad Sohail & Jaehyun Park & Jun Young Kim & Heung Soo Kim & Jaehun Lee, 2022. "Modified Whiteside’s Line-Based Transepicondylar Axis for Imageless Total Knee Arthroplasty," Mathematics, MDPI, vol. 10(19), pages 1-17, October.
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