Biological variability in biomechanical engineering research: Significance and meta-analysis of current modeling practices

Cook, Douglas D.; Julias, Margaret; Nauman, Eric A.

doi:10.1016/j.jbiomech.2014.01.040

Cited by 33 publications

(15 citation statements)

References 103 publications

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“…An example of such surgical techniques can be seen in some arthroscopic cartilage repair surgeries when surgeons use carbon dioxide or normal air insufflation to facilitate the application of the gel under dry conditions [61] . Moreover, advanced real-time imaging or geometric feedback modalities, along with computational modeling and analysis techniques, are of equal importance in the success of in vivo bioprinting, for fast and accurate morphological assessment of defects and newly fabricated constructs [62][63][64][65] .…”

Section: Prospects For the Futurementioning

confidence: 99%

The trend towards in vivo bioprinting

Wang¹,

He²,

Liu³

et al. 2024

IJB

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Bioprinting is one of several newly emerged tissue engineering strategies that hold great promise in alleviating of organ shortage crisis. To date, a range of living biological constructs have already been fabricated in vitro using this technology. However, an in vitro approach may have several intrinsic limitations regarding its clinical applicability in some cases. A possible solution is in vivo bioprinting, in which the de novo tissues/organs are to be directly fabricated and positioned at the damaged site in the living body. This strategy would be particularly effective in the treatment of tissues/organs that can be safely arrested and immobilized during bioprinting. Proof-of-concept studies on in vivo bioprinting have been reported recently, on the basis of which this paper reviews the current state-of-the-art bioprinting technologies with a particular focus on their advantages and challenges for the in vivo application.

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Section: Prospects For the Futurementioning

confidence: 99%

The trend towards in vivo bioprinting

Wang¹,

He²,

Liu³

et al. 2024

IJB

View full text Add to dashboard Cite

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“…In order to assess the quality of the obtained results a baseline was necessary. In this study, this baseline was computed using a typical approach to sensitivity analysis found in many biomechanical studies [3]. The model was solved 43 times.…”

Section: Computing the Baselinementioning

confidence: 99%

“…To make the models clinically-viable, it is important to understand how these uncertainties affect the results or-in other words-what outputs can be expected of the model, when the uncertainties in the parameter acquisition and determination are taken into account. Although, the trend to account for variability of the model parameters has 2 of 12 already been noted in some recent studies on biomechanical modeling [3], most of the available studies on the ankle joint modeling [4][5][6][7][8][9][10][11][12][13] have not focused on this issue.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Uncertainty of an Ankle Joint Model with Genetic Algorithm

Ciszkiewicz

2020

Materials

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Recent studies in biomechanical modeling suggest a paradigm shift, in which the parameters of biomechanical models would no longer treated as fixed values but as random variables with, often unknown, distributions. In turn, novel and efficient numerical methods will be required to handle such complicated modeling problems. The main aim of this study was to introduce and verify genetic algorithm for analyzing uncertainty in biomechanical modeling. The idea of the method was to encode two adversarial models within one decision variable vector. These structures would then be concurrently optimized with the objective being the maximization of the difference between their outputs. The approach, albeit expensive numerically, offered a general formulation of the uncertainty analysis, which did not constrain the search space. The second aim of the study was to apply the proposed procedure to analyze the uncertainty of an ankle joint model with 43 parameters and flexible links. The bounds on geometrical and material parameters of the model were set to 0.50 mm and 5.00% respectively. The results obtained from the analysis were unexpected. The two obtained adversarial structures were almost visually indistinguishable and differed up to 38.52% in their angular displacements.

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“…The influence of physiological variation on model predictions have been found to be significant in other biomechanical models (Cook et al, 2014), but the influence of physiological variation in IVK parameters has not yet been quantified. The goal of this study was to quantify variation in model estimates of extension strength, flexion strength and gravitational demand (the ratio of gravitational load to muscular capacity) in the sagittal plane due to variation in IVK.…”

Section: Introductionmentioning

confidence: 99%

Inter-individual variation in vertebral kinematics affects predictions of neck musculoskeletal models

Nevins

Zheng

Vasavada

2014

Journal of Biomechanics

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Experimental studies have found significant variation in cervical intervertebral kinematics (IVK) among healthy subjects, but the effect of this variation on biomechanical properties, such as neck strength, has not been explored. The goal of this study was to quantify variation in model predictions of extension strength, flexion strength and gravitational demand (the ratio of gravitational load from the weight of the head to neck muscle extension strength), due to inter-subject variation in IVK. IVK were measured from sagittal radiographs of twenty-four subjects (14F, 10M) in five postures: maximal extension, mid-extension, neutral, mid-flexion, and maximal flexion. IVK were defined by the position (anterior-posterior and superior-inferior) of each cervical vertebra with respect to T1 and its angle with respect to horizontal, and fit with a cubic polynomial over the range of motion. The IVK of each subject were scaled and incorporated into musculoskeletal models to create models that were identical in muscle force- and moment-generating properties but had subject-specific kinematics. The effect of inter-subject variation in IVK was quantified using the coefficient of variation (COV), the ratio of the standard deviation to the mean. COV of extension strength ranged from 8 – 15% over the range of motion, but COV of flexion strength were 20 – 80%. Moreover, the COV of gravitational demand was 80 – 90%, because the gravitational demand is affected by head position as well as neck strength. These results indicate that including inter-individual variation in models is important for evaluating neck musculoskeletal biomechanical properties.

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Biological variability in biomechanical engineering research: Significance and meta-analysis of current modeling practices

Cited by 33 publications

References 103 publications

The trend towards in vivo bioprinting

The trend towards in vivo bioprinting

Analyzing Uncertainty of an Ankle Joint Model with Genetic Algorithm

Inter-individual variation in vertebral kinematics affects predictions of neck musculoskeletal models

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