Prediction of the fracture load of whole proximal femur specimens by topological analysis of the mineral distribution in DXA-scan images

Boehm, Heike; Horng, Annie; Notohamiprodjo, Mike; Eckstein, F.; Bürklein, Dominik; Panteleon, A.; Lutz, Juergen; Reiser, Maximilian

doi:10.1016/j.bone.2008.07.244

Cited by 25 publications

(20 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although for regularly shaped specimens of bone (e.g., trabecular bone cubes) the exponent c has been shown to lie between two and three, for intact proximal femurs that are tested to failure the exponent is smaller (between one and two) [3,7,40]. This may be a result of the different ''densities'' employed.…”

Section: Non-bayesian Modelmentioning

confidence: 94%

“…Numerous experimental studies have demonstrated that the strength of bone can be related to its BMD by a power law function [3,7,10,40,51]:…”

Section: Non-bayesian Modelmentioning

confidence: 99%

“…To apply the model (3)(4)(5) to individual subjects, information on the fall rate and conditional probability of fracture are required. First, consider the fall rate, k; this parameter contains all the information regarding the likelihood of a fall in a given time period for a given individual.…”

Section: Non-bayesian Modelmentioning

confidence: 99%

See 2 more Smart Citations

A poisson process model for hip fracture risk

Schechner

Luo

Kaufman

et al. 2010

Med Biol Eng Comput

View full text Add to dashboard Cite

The primary method for assessing fracture risk in osteoporosis relies primarily on measurement of bone mass. Estimation of fracture risk is most often evaluated using logistic or proportional hazards models. Notwithstanding the success of these models, there is still much uncertainty as to who will or will not suffer a fracture. This has led to a search for other components besides mass that affect bone strength. The purpose of this paper is to introduce a new mechanistic stochastic model that characterizes the risk of hip fracture in an individual. A Poisson process is used to model the occurrence of falls, which are assumed to occur at a rate, lambda. The load induced by a fall is assumed to be a random variable that has a Weibull probability distribution. The combination of falls together with loads leads to a compound Poisson process. By retaining only those occurrences of the compound Poisson process that result in a hip fracture, a thinned Poisson process is defined that itself is a Poisson process. The fall rate is modeled as an affine function of age, and hip strength is modeled as a power law function of bone mineral density (BMD). The risk of hip fracture can then be computed as a function of age and BMD. By extending the analysis to a Bayesian framework, the conditional densities of BMD given a prior fracture and no prior fracture can be computed and shown to be consistent with clinical observations. In addition, the conditional probabilities of fracture given a prior fracture and no prior fracture can also be computed, and also demonstrate results similar to clinical data. The model elucidates the fact that the hip fracture process is inherently random and improvements in hip strength estimation over and above that provided by BMD operate in a highly "noisy" environment and may therefore have little ability to impact clinical practice.

show abstract

Section: Non-bayesian Modelmentioning

confidence: 94%

“…Numerous experimental studies have demonstrated that the strength of bone can be related to its BMD by a power law function [3,7,10,40,51]:…”

Section: Non-bayesian Modelmentioning

confidence: 99%

See 1 more Smart Citation

A poisson process model for hip fracture risk

Schechner

Luo

Kaufman

et al. 2010

Med Biol Eng Comput

View full text Add to dashboard Cite

show abstract

“…To do so, 20 human proximal femora were prepared with a strain rosette glued on the antero-superior femoral neck, and a speckle pattern was airbrushed over the same area. Femora were loaded to 50% of their predicted fracture load (determined using the method proposed by Boehm et al, [46]) at 0.5 mm/s. Images were recorded at 100 fps with 1280x800 px (approx.…”

Section: Most Relevant Studiesmentioning

confidence: 99%

Extracting accurate strain measurements in bone mechanics: A critical review of current methods

Grassi

Isaksson

2015

Journal of the Mechanical Behavior of Biomedical Materials

View full text Add to dashboard Cite

Osteoporosis related fractures are a social burden that advocates for more accurate fracture prediction methods. Mechanistic methods, e.g. finite element models, have been proposed as a tool to better predict bone mechanical behaviour and strength. However, there is little consensus about the optimal constitutive law to describe bone as a material. Extracting reliable and relevant strain data from experimental tests is of fundamental importance to better understand bone mechanical properties, and to validate numerical models.Several techniques have been used to measure strain in experimental mechanics, with substantial differences in terms of accuracy, precision, time-and length-scale. Each technique presents upsides and downsides that must be carefully evaluated when designing the experiment. Moreover, additional complexities are often encountered when applying such strain measurement techniques to bone, due to its complex composite structure. 2This review of literature examined the four most commonly adopted methods for strain measurements (strain gauges, fibre Bragg grating sensors, digital image correlation, and digital volume correlation), with a focus on studies with bone as a substrate material, at the organ and tissue level. For each of them the working principles, a summary of the main applications to bone mechanics at the organ-and tissue-level, and a list of pros and cons are provided.

show abstract

“…These researchers have identified the roles of bone density (Lotz and Hayes, 1990;Courtney et al, 1995;Leichter et al, 2001;Lochmüller et al, 2002;Eckstein et al, 2004;Boehm et al, 2008;Manske et al, 2008;de Bakker et al, 2009;Pulkkinen et al, 2008), posture (Pinilla et al, 1996), displacement and strain rate (Weber et al, 1992;Courtney et al, 1994), geometry (Cheng et al, 1997), and loading configuration (Keyak et al, 1998;Keyak, 2000). While these studies have contributed greatly to the current Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/jbiomech www.JBiomech.com understanding of hip fracture, they used constant displacement rate protocols.…”

Section: Introductionmentioning

confidence: 96%