Expediting finite element analyses for subject‐specific studies of knee osteoarthritis : A literature review
(2021) In Applied Sciences (Switzerland) 11(23).- 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 pipe-line consists of three well‐established phases: pre‐processing, processing, and post‐processing. Cur-rently, these phases are time‐consuming, making the FEA... (More)
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 pipe-line consists of three well‐established phases: pre‐processing, processing, and post‐processing. Cur-rently, 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.
(Less)
- author
- Paz, Alexander ; Orozco, Gustavo A. LU ; Korhonen, Rami K. ; García, José J. and Mononen, Mika E.
- organization
- publishing date
- 2021-12-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Articular cartilage, Finite element analysis, Knee joint, Osteoarthritis
- in
- Applied Sciences (Switzerland)
- volume
- 11
- issue
- 23
- article number
- 11440
- pages
- 24 pages
- publisher
- MDPI AG
- external identifiers
-
- scopus:85120725878
- ISSN
- 2076-3417
- DOI
- 10.3390/app112311440
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: This research was funded by the Academy of Finland (grants 324994, 328920), the Sigrid Juselius Foundation, and the Swedish Research Council (2019?00953?under the frame of ERA PerMed). Funding Information: Funding: This research was funded by the Academy of Finland (grants 324994, 328920), the Sigrid Juselius Foundation, and the Swedish Research Council (2019‐00953—under the frame of ERA PerMed). Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.
- id
- 6a6a45ff-05b7-424a-b2a4-b89b2fd2933f
- date added to LUP
- 2022-04-25 09:43:37
- date last changed
- 2022-05-03 15:43:28
@article{6a6a45ff-05b7-424a-b2a4-b89b2fd2933f,
abstract = {{<p>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 pipe-line consists of three well‐established phases: pre‐processing, processing, and post‐processing. Cur-rently, 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.</p>}},
author = {{Paz, Alexander and Orozco, Gustavo A. and Korhonen, Rami K. and García, José J. and Mononen, Mika E.}},
issn = {{2076-3417}},
keywords = {{Articular cartilage; Finite element analysis; Knee joint; Osteoarthritis}},
language = {{eng}},
month = {{12}},
number = {{23}},
publisher = {{MDPI AG}},
series = {{Applied Sciences (Switzerland)}},
title = {{Expediting finite element analyses for subject‐specific studies of knee osteoarthritis : A literature review}},
url = {{http://dx.doi.org/10.3390/app112311440}},
doi = {{10.3390/app112311440}},
volume = {{11}},
year = {{2021}},
}