Neuro-musculoskeletal flexible multibody simulation yields a framework for efficient bone failure risk assessment.
Andreas GeierMaeruan KebbachEhsan SoodmandChristoph WoernleDaniel KluessRainer BaderPublished in: Scientific reports (2019)
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.
Keyphrases
- bone mineral density
- postmenopausal women
- hip fracture
- risk assessment
- body composition
- electronic health record
- decision making
- finite element
- climate change
- computed tomography
- magnetic resonance imaging
- human health
- escherichia coli
- soft tissue
- systematic review
- endothelial cells
- bone loss
- palliative care
- molecular docking
- machine learning
- deep learning
- magnetic resonance
- high resolution
- dual energy
- pet ct
- breast cancer risk