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Neuro-musculoskeletal Flexible Multibody Simulation Yields a Framework for Efficient Bone Failure Risk Assessment

Overview
Journal Sci Rep
Specialty Science
Date 2019 May 8
PMID 31061388
Citations 6
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Abstract

Fragility fractures are a major socioeconomic problem. A non-invasive, computationally-efficient method for the identification of fracture risk scenarios under the representation of neuro-musculoskeletal dynamics does not exist. We introduce a computational workflow that integrates modally-reduced, quantitative CT-based finite-element models into neuro-musculoskeletal flexible multibody simulation (NfMBS) for early bone fracture risk assessment. Our workflow quantifies the bone strength via the osteogenic stresses and strains that arise due to the physiological-like loading of the bone under the representation of patient-specific neuro-musculoskeletal dynamics. This allows for non-invasive, computationally-efficient dynamic analysis over the enormous parameter space of fracture risk scenarios, while requiring only sparse clinical data. Experimental validation on a fresh human femur specimen together with femur strength computations that were consistent with literature findings provide confidence in the workflow: The simulation of an entire squat took only 38 s CPU-time. Owing to the loss (16% cortical, 33% trabecular) of bone mineral density (BMD), the strain measure that is associated with bone fracture increased by 31.4%; and yielded an elevated risk of a femoral hip fracture. Our novel workflow could offer clinicians with decision-making guidance by enabling the first combined in-silico analysis tool using NfMBS and BMD measurements for optimized bone fracture risk assessment.

Citing Articles

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References
1.
Zysset P, DallAra E, Varga P, Pahr D . Finite element analysis for prediction of bone strength. Bonekey Rep. 2014; 2:386. PMC: 3765052. DOI: 10.1038/bonekey.2013.120. View

2.
Carbone V, Fluit R, Pellikaan P, van der Krogt M, Janssen D, Damsgaard M . TLEM 2.0 - a comprehensive musculoskeletal geometry dataset for subject-specific modeling of lower extremity. J Biomech. 2015; 48(5):734-41. DOI: 10.1016/j.jbiomech.2014.12.034. View

3.
Taylor W, Roland E, Ploeg H, Hertig D, Klabunde R, Warner M . Determination of orthotropic bone elastic constants using FEA and modal analysis. J Biomech. 2002; 35(6):767-73. DOI: 10.1016/s0021-9290(02)00022-2. View

4.
Venalainen M, Mononen M, Salo J, Rasanen L, Jurvelin J, Toyras J . Quantitative Evaluation of the Mechanical Risks Caused by Focal Cartilage Defects in the Knee. Sci Rep. 2016; 6:37538. PMC: 5126640. DOI: 10.1038/srep37538. View

5.
Cong A, Op den Buijs J, Dragomir-Daescu D . In situ parameter identification of optimal density-elastic modulus relationships in subject-specific finite element models of the proximal femur. Med Eng Phys. 2010; 33(2):164-73. PMC: 3045472. DOI: 10.1016/j.medengphy.2010.09.018. View