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Paz A, Lavikainen J, Turunen MJ, García JJ, Korhonen RK, Mononen ME. Knee-Loading Predictions with Neural Networks Improve Finite Element Modeling Classifications of Knee Osteoarthritis: Data from the Osteoarthritis Initiative. Ann Biomed Eng 2024:10.1007/s10439-024-03549-2. [PMID: 38842728 DOI: 10.1007/s10439-024-03549-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 05/20/2024] [Indexed: 06/07/2024]
Abstract
Physics-based modeling methods have the potential to investigate the mechanical factors associated with knee osteoarthritis (OA) and predict the future radiographic condition of the joint. However, it remains unclear what level of detail is optimal in these methods to achieve accurate prediction results in cohort studies. In this work, we extended a template-based finite element (FE) method to include the lateral and medial compartments of the tibiofemoral joint and simulated the mechanical responses of 97 knees under three conditions of gait loading. Furthermore, the effects of variations in cartilage thickness and failure equation on predicted cartilage degeneration were investigated. Our results showed that using neural network-based estimations of peak knee loading provided classification performances of 0.70 (AUC, p < 0.05) in distinguishing between knees that developed severe OA or mild OA and knees that did not develop OA eight years after a healthy radiographic baseline. However, FE models incorporating subject-specific femoral and tibial cartilage thickness did not improve this classification performance, suggesting there exists an optimal point between personalized loading and geometry for discrimination purposes. In summary, we proposed a modeling framework that streamlines the rapid generation of individualized knee models achieving promising classification performance while avoiding motion capture and cartilage image segmentation.
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Affiliation(s)
- Alexander Paz
- Department of Technical Physics, University of Eastern Finland, Yliopistonranta 1, 70211, Kuopio, Finland.
- Escuela de Ingeniería Civil y Geomática, Universidad del Valle, Cali, Colombia.
| | - Jere Lavikainen
- Department of Technical Physics, University of Eastern Finland, Yliopistonranta 1, 70211, Kuopio, Finland
- Diagnostic Imaging Center, Wellbeing Services County of North Savo, Kuopio, Finland
| | - Mikael J Turunen
- Department of Technical Physics, University of Eastern Finland, Yliopistonranta 1, 70211, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Wellbeing Services County of North Savo, Kuopio, Finland
| | - José J García
- Escuela de Ingeniería Civil y Geomática, Universidad del Valle, Cali, Colombia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Yliopistonranta 1, 70211, Kuopio, Finland
| | - Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, Yliopistonranta 1, 70211, Kuopio, Finland
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Mononen ME, Liukkonen MK, Turunen MJ. X-ray with finite element analysis is a viable alternative for MRI to predict knee osteoarthritis: Data from the Osteoarthritis Initiative. J Orthop Res 2024. [PMID: 38650428 DOI: 10.1002/jor.25861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/29/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
Magnetic resonance imaging (MRI) offers superior soft tissue contrast compared to clinical X-ray imaging methods, while also providing accurate three-dimensional (3D) geometries, it could be reasoned to be the best imaging modality to create 3D finite element (FE) geometries of the knee joint. However, MRI may not necessarily be superior for making tissue-level FE simulations of internal stress distributions within knee joint, which can be utilized to calculate subject-specific risk for the onset and development of knee osteoarthritis (KOA). Specifically, MRI does not provide any information about tissue stiffness, as the imaging is usually performed with the patient lying on their back. In contrast, native X-rays taken while the patient is standing indirectly reveal information of the overall health of the knee that is not seen in MRI. To determine the feasibility of X-ray workflow to generate FE models based on the baseline information (clinical image data and subject characteristics), we compared MRI and X-ray-based simulations of volumetric cartilage degenerations (N = 1213) against 8-year follow-up data. The results suggest that X-ray-based predictions of KOA are at least as good as MRI-based predictions for subjects with no previous knee injuries. This finding may have important implications for preventive care, as X-ray imaging is much more accessible than MRI.
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Affiliation(s)
- Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Mimmi K Liukkonen
- Department of Clinical Radiology, Kuopio University Hospital, The Wellbeing Services County of North Savo, Kuopio, Finland
| | - Mikael J Turunen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, The Wellbeing Services County of North Savo, Kuopio, Finland
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Diamond LE, Grant T, Uhlrich SD. Osteoarthritis year in review 2023: Biomechanics. Osteoarthritis Cartilage 2024; 32:138-147. [PMID: 38043858 DOI: 10.1016/j.joca.2023.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 12/05/2023]
Abstract
Biomechanics plays a significant yet complex role in osteoarthritis (OA) onset and progression. Identifying alterations in biomechanical factors and their complex interactions is critical for gaining new insights into OA pathophysiology and identification of clearly defined and modifiable mechanical treatment targets. This review synthesized biomechanics studies from March 2022 to April 2023, from which three themes relating to human gait emerged: (1) new insights into the pathogenesis of OA using computational modeling and machine learning, (2) technology-enhanced biomechanical interventions for OA, and (3) out-of-lab biomechanical assessments of OA. We further highlighted future-focused areas which may continue to advance the field of biomechanics in OA, with a particular emphasis on exploiting technology to understand and treat biomechanical mechanisms of OA outside the laboratory. The breadth of studies included in this review highlights the complex role of biomechanics in OA and showcase numerous innovative and outstanding contributions to the field. Exciting cross-disciplinary efforts integrating computational modeling, mobile sensors, and machine learning methods show great promise for streamlining in vivo multi-scale biomechanics workflows and are expected to underpin future breakthroughs in the understanding and treatment of biomechanics in OA.
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Affiliation(s)
- Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Tamara Grant
- Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
| | - Scott D Uhlrich
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
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Paz A, García JJ, Korhonen RK, Mononen ME. Towards a Transferable Modeling Method of the Knee to Distinguish Between Future Healthy Joints from Osteoarthritic Joints: Data from the Osteoarthritis Initiative. Ann Biomed Eng 2023; 51:2192-2203. [PMID: 37284996 PMCID: PMC10518288 DOI: 10.1007/s10439-023-03252-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/22/2023] [Indexed: 06/08/2023]
Abstract
Computational models can be used to predict the onset and progression of knee osteoarthritis. Ensuring the transferability of these approaches among computational frameworks is urgent for their reliability. In this work, we assessed the transferability of a template-based modeling strategy, based on the finite element (FE) method, by implementing it on two different FE softwares and comparing their results and conclusions. For that, we simulated the knee joint cartilage biomechanics of 154 knees using healthy baseline conditions and predicted the degeneration that occurred after 8 years of follow-up. For comparisons, we grouped the knees using their Kellgren-Lawrence grade at the 8-year follow-up time and the simulated volume of cartilage tissue that exceeded age-dependent thresholds of maximum principal stress. We considered the medial compartment of the knee in the FE models and used ABAQUS and FEBio FE softwares for simulations. The two FE softwares detected different volumes of overstressed tissue in corresponding knee samples (p < 0.01). However, both programs correctly distinguished between the joints that remained healthy and those that developed severe osteoarthritis after the follow-up (AUC = 0.73). These results indicate that different software implementations of a template-based modeling method similarly classify future knee osteoarthritis grades, motivating further evaluations using simpler cartilage constitutive models and additional studies on the reproducibility of these modeling strategies.
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Affiliation(s)
- Alexander Paz
- Department of Technical Physics, University of Eastern Finland, Yliopistonranta 1, 70211, Kuopio, Finland.
- Escuela de Ingeniería Civil y Geomática, Universidad del Valle, Cali, Colombia.
| | - José J García
- Escuela de Ingeniería Civil y Geomática, Universidad del Valle, Cali, Colombia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Yliopistonranta 1, 70211, Kuopio, Finland
| | - Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, Yliopistonranta 1, 70211, Kuopio, Finland
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