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Review

Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review

1
Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland
2
Escuela de Ingeniería Civil y Geomática, Universidad del Valle, Cali 76001, Colombia
3
Department of Biomedical Engineering, Lund University, P.O. Box 188, 22100 Lund, Sweden
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(23), 11440; https://doi.org/10.3390/app112311440
Submission received: 13 October 2021 / Revised: 19 November 2021 / Accepted: 24 November 2021 / Published: 2 December 2021
(This article belongs to the Special Issue Finite Element Modeling of Joint)

Abstract

Osteoarthritis (OA) is a degenerative disease that affects the synovial joints, especially the knee joint, diminishing the ability of patients to perform daily physical activities. Unfortunately, there is no cure for this nearly irreversible musculoskeletal disorder. Nowadays, many researchers aim for in silico-based methods to simulate personalized risks for the onset and progression of OA and evaluate the effects of different conservative preventative actions. Finite element analysis (FEA) has been considered a promising method to be developed for knee OA management. The FEA pipeline consists of three well-established phases: pre-processing, processing, and post-processing. Currently, these phases are time-consuming, making the FEA workflow cumbersome for the clinical environment. Hence, in this narrative review, we overviewed present-day trends towards clinical methods for subject-specific knee OA studies utilizing FEA. We reviewed studies focused on understanding mechanisms that initiate knee OA and expediting the FEA workflow applied to the whole-organ level. Based on the current trends we observed, we believe that forthcoming knee FEAs will provide nearly real-time predictions for the personalized risk of developing knee OA. These analyses will integrate subject-specific geometries, loading conditions, and estimations of local tissue mechanical properties. This will be achieved by combining state-of-the-art FEA workflows with automated approaches aided by machine learning techniques.
Keywords: osteoarthritis; knee joint; articular cartilage; finite element analysis osteoarthritis; knee joint; articular cartilage; finite element analysis

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MDPI and ACS Style

Paz, A.; Orozco, G.A.; Korhonen, R.K.; García, J.J.; Mononen, M.E. Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review. Appl. Sci. 2021, 11, 11440. https://doi.org/10.3390/app112311440

AMA Style

Paz A, Orozco GA, Korhonen RK, García JJ, Mononen ME. Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review. Applied Sciences. 2021; 11(23):11440. https://doi.org/10.3390/app112311440

Chicago/Turabian Style

Paz, Alexander, Gustavo A. Orozco, Rami K. Korhonen, José J. García, and Mika E. Mononen. 2021. "Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review" Applied Sciences 11, no. 23: 11440. https://doi.org/10.3390/app112311440

APA Style

Paz, A., Orozco, G. A., Korhonen, R. K., García, J. J., & Mononen, M. E. (2021). Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review. Applied Sciences, 11(23), 11440. https://doi.org/10.3390/app112311440

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