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Expediting finite e...
Expediting finite element analyses for subject‐specific studies of knee osteoarthritis : A literature review
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- Paz, Alexander (author)
- University of Eastern Finland
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- Orozco, Gustavo A. (author)
- Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,University of Eastern Finland
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- Korhonen, Rami K. (author)
- University of Eastern Finland
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- García, José J. (author)
- Universidad del Valle
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- Mononen, Mika E. (author)
- University of Eastern Finland
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(creator_code:org_t)
- 2021-12-02
- 2021
- English 24 s.
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In: Applied Sciences (Switzerland). - : MDPI AG. - 2076-3417. ; 11:23
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Abstract
Subject headings
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- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinsk bioteknologi -- Biomedicinsk laboratorievetenskap/teknologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Medical Biotechnology -- Biomedical Laboratory Science/Technology (hsv//eng)
Keyword
- Articular cartilage
- Finite element analysis
- Knee joint
- Osteoarthritis
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