Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty
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 softwa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/16/3527 |
| _version_ | 1797583961223856128 |
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| author | Jun Young Kim Muhammad Sohail Heung Soo Kim |
| author_facet | Jun Young Kim Muhammad Sohail Heung Soo Kim |
| author_sort | Jun Young Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:45:34Z |
| format | Article |
| id | doaj.art-64ce4b6fa40341f9af4b0758d28dede4 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T23:45:34Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-64ce4b6fa40341f9af4b0758d28dede42023-11-19T02:03:20ZengMDPI AGMathematics2227-73902023-08-011116352710.3390/math11163527Rapid Estimation of Contact Stresses in Imageless Total Knee ArthroplastyJun Young Kim0Muhammad Sohail1Heung Soo Kim2Department of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical Engineering, Dongguk University-Seoul, 30 Pildong-ro 1gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaTotal 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.https://www.mdpi.com/2227-7390/11/16/3527imageless navigatortotal knee arthroplastyfinite element analysismachine learning |
| spellingShingle | Jun Young Kim Muhammad Sohail Heung Soo Kim Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty Mathematics imageless navigator total knee arthroplasty finite element analysis machine learning |
| title | Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty |
| title_full | Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty |
| title_fullStr | Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty |
| title_full_unstemmed | Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty |
| title_short | Rapid Estimation of Contact Stresses in Imageless Total Knee Arthroplasty |
| title_sort | rapid estimation of contact stresses in imageless total knee arthroplasty |
| topic | imageless navigator total knee arthroplasty finite element analysis machine learning |
| url | https://www.mdpi.com/2227-7390/11/16/3527 |
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