Assessment Of Bone Fracture Risk Research Articles

The Application of Advanced Bone Imaging Technologies in Sports Medicine

Until recently, the evaluation of bone health and fracture risk through imaging has been limited to dual-energy X-ray absorptiometry (DXA) and plain radiographs, with a limited application in the athletic population. Several novel imaging technologies are now available for the clinical assessment of bone health, including bone injury risk and healing progression, with a potential for use in sports medicine. Among these imaging modalities is high-resolution peripheral quantitative computed tomography (HR-pQCT) which is a promising technology that has been developed to examine the bone microarchitecture in both cortical and trabecular bone at peripheral anatomical sites. Technologies that do not expose patients to ionizing radiation are optimal, particularly for athletes who may require frequent imaging. One such alternative is diagnostic ultrasound, which is preferable due to its low cost and lack of radiation exposure. Furthermore, ultrasound, which has not been a common imaging modality for monitoring fracture healing, has been shown to potentially demonstrate earlier signs of union compared to conventional radiographs, including callus mineralization and density at the healing site. Through the use of conventional magnetic resonance imaging (MRI), finite element analysis (FEA) can be used to simulate the structural and mechanical properties of bone. On the other hand, the ultrashort echo time (UTE) MRI can evaluate cortical bone quality by detecting water bound to the organic bone matrix and free water, providing important information about bone porosity. Several novel bone imaging techniques originally developed for osteoporosis assessment have great potential to be utilized to improve the standard of care in bone fracture risk assessment and healing in sports medicine with much greater precision and less adverse radiation exposure.

FRAX assessment in people ageing with HIV.

Current British HIV Association (BHIVA) guidelines recommend the use of FRAX for the routine assessment of bone fracture risk in people living with HIV over 50years of age every 3years. Bone mineral density measurement with dual-energy X-ray absorptiometry (DXA) scan is recommended for those with increased fracture risk (FRAX major >10%). Our objectives were to estimate the prevalence of and risk factors for osteoporosis in a population of PLWH aged>50years and assess the utility of FRAX in predicting the presence of DXA-proven osteoporosis in this cohort. This was a cross-sectional study of a cohort of PLWH aged >50years attending the Chelsea and Westminster Hospital and who had a DXA scan between January 2009 and December 2018. FRAX scores were calculated using the Sheffield algorithm. Multiple regression models and Cohen's kappa values were used to assess risk factors for osteoporosis and agreement between FRAX and DXA scan results, respectively. In all, 744 patients were included (92.9% male, mean age 56±5years). The prevalence rates of osteoporosis (at DXA scans) and osteopenia were 12.2% and 63.7%, respectively. FRAX major was >10% in only two patients, while 90/91 (98.9%) patients with osteoporosis had a normal FRAX score. The presence of osteoporosis was significantly associated with low body mass index and estimated glomerular filtration rate (p<0.05). Our results indicate that FRAX scores did not predict the presence of osteoporosis in our population of PLWH over 50years of age and therefore FRAX scores may not be the appropriate tool to define eligibility to perform DXA scans in PLWH.

Can DXA image-based deep learning model predict the anisotropic elastic behavior of trabecular bone?

3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the recent success of deep learning (DL) techniques, we hypothesized that DL modeling techniques could be used to predict the apparent stiffness tensor of trabecular bone directly using dual-energy X-ray absorptiometry (DXA) images. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the apparent stiffness tensor of trabecular bone cubes using their DXA images. Trabecular bone cubes obtained from human cadaver proximal femurs were used to obtain simulated DXA images as input, and the apparent stiffness tensor of the trabecular cubes determined by using micro-CT based FE simulations was used as output (ground truth) to train the DL model. The prediction accuracy of the DL model was evaluated by comparing it with the micro-CT based FE models, histomorphometric parameter based multiple linear regression models, and BV/TV/fabric tensor based multiple linear regression models. The results showed that DXA image-based DL model achieved high fidelity in predicting the apparent stiffness tensor of trabecular bone cubes (R2 = 0.905–0.973), comparable to or better than the histomorphometric parameter based multiple linear regression and BV/TV/fabric tensor based multiple linear regression models, thus supporting the hypothesis of this study. The outcome of this study could be used to help develop DXA image-based DL techniques for clinical assessment of bone fracture risk.

Anatomical variation in intracortical canal network microarchitecture and its influence on bone fracture risk

Intracortical canals are a major contributor to cortical bone porosity and influence its mechanical response. Canal networks act as stress concentrators and the magnitude of which depends on the size and spatial distribution of canals. In the present study, we investigated site-dependent variation in intracortical canal network morphological indices and their effect on the mechanical response of bone. For this, mid-diaphysis of rat tibia bones were scanned using high-resolution micro-CT and morphological indices were measured for four main anatomical sites-anterior, posterior, medial and lateral. Further, a micro-finite element (μFE) model was developed to quantify the stress concentration regions in different cortices. The fracture risk was assessed using an effective strain approach. Results show that canal porosity, canal orientation and canal length are site-dependent whereas canal diameter and canal number density are independent of the site. The lateral cortex has significantly higher porosity compared to the posterior cortex (p < 0.05). The orientation of canals is found significantly different between endosteal and periosteal regions for anterior and medial quadrants. Canals are inclined at higher angles with bone axis in the endosteal region as compare to the periosteal region. The μ-FE results show that the regions with higher effective strain are concentrated around the canals. Further, failed element volume per unit bone volume is found highest for medial cortex whereas lowest for posterior cortex. The higher failed volume is associated with more radial canals in the medial cortex as compare to other cortices. The linear regression analysis shows that the volume of overstrained elements strongly depends on canal orientation (R2 = 0.73, p < 0.0001) and canal porosity (R2 = 0.61, p < 0.0001). The findings from this study suggest that along with vascular canal porosity, canal orientation and canal diameter can further improve the bone fracture risk assessment.

EquiSim: An Open-Source Articulatable Statistical Model of the Equine Distal Limb.

Most digital models of the equine distal limb that are available in the community are static and/or subject specific; hence, they have limited applications in veterinary research. In this paper, we present an articulatable model of the entire equine distal limb based on statistical shape modeling. The model describes the inter-subject variability in bone geometry while maintaining proper jointspace distances to support model articulation toward different poses. Shape variation modes are explained in terms of common biometrics in order to ease model interpretation from a veterinary point of view. The model is publicly available through a graphical user interface (https://github.com/jvhoutte/equisim) in order to facilitate future digitalization in veterinary research, such as computer-aided designs, three-dimensional printing of bone implants, bone fracture risk assessment through finite element methods, and data registration and segmentation problems for clinical practices.

Autonomous FEs (AFE) - A stride toward personalized medicine

Finite element analysis (FEA), introduced more than half a century ago, requires a (qualified) analyst to generate the necessary input data, verify the output and post process the analysis results for a meaningful conclusion. The required expertise and labor efforts have precluded the use of FEA in daily medical practice by orthopedic surgeons for example.Patient-specific analyses of the mechanical response of human bones may have a tremendous impact in clinical practice should they be easily accessible by orthopedic surgeons. Recent scientific advancements such as low dose CT scans, machine learning, and high order FEA which facilitates an inherent methodology for assessing numerical accuracy allow a fully autonomous process for assessing bone strength and fracture risk. This autonomous process, that we shall refer to here as “Autonomous Finite Element” (AFE) analysis, introduces a paradigm shift in the use of FEA.We shall describe herein a novel process that utilizes AFE to produce a patient-specific assessment of bone strength. The process consists of an automatic segmentation of femurs from CT-scans by convolution neural networks, an automatic mesh generation and application of boundary conditions based on anatomical points, a high-order FE analysis with numerical error control, and finally an automatic report with a clear assessment of bone fracture risk. One specific application of AFE is the determination of the risk of fracture for patients with tumors of the femur and whether a prophylactic surgery is needed. To the best of our knowledge this is the first CE accredited AFE application being used by orthopedic surgeons in clinical practice.

Neuro-musculoskeletal flexible multibody simulation yields a framework for efficient bone failure risk assessment

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.

Integrating Test Data and ATBM Simulations into Dose Propagation Uncertainty Formulation for Bone Fracture Risk Assessment

We consider the problem of assessing bone fracture risk for a subject hit by a blunt impact projectile. We aim at constructing a framework for integrating test data and Advanced Total Body Model (ATBM) simulations into the risk assessment. The ATBM is a finite element model managed by the Joint Non-Lethal Weapons Directorate for the purpose of assessing the risk of injury caused by blunt impacts from non-lethal weapons. In ATBM simulations, the quantity that determines arm bone fracture is the calculated maximum strain in the bone. The main obstacle to accurate prediction is that the calculated strain is incompatible with the measured strain. The fracture strain measured in bending tests of real bones is affected by random inhomogeneity in bones and uncertainty in measurement gauge attachment location/orientation. In contrast, the strain calculated in ATBM simulations is based on the assumption that all bones are perfectly elastic with homogeneous material properties and no measurement uncertainty. To connect test data and ATBM simulations in a proper and meaningful setting, we introduce the concept of elasticity-homogenized strain. We interpret test data in terms of the homogenized strain, and build an empirical dose-injury model with the homogenized strain as the input dose for predicting injury. The maximum strain calculated by ATBM has randomness due to uncertainty in specifications of ATBM setup parameters. The dose propagation uncertainty formulation accommodates this uncertainty efficiently by simply updating the shape parameters in the dose-injury model, avoiding the high computational cost of sampling this uncertainty via multiple ATBM runs.

Trabecular Bone Score (TBS) in Clinical Practice - Rewiev.

Bone mineral density (BMD) assessment is the basic method for assessing fracture risk and diagnosing osteoporosis according to the World Health Organization (WHO). Osteoporosis is diagnosed when the T-score at the proximal femur or spine is T≤-2.5; however, 70% of fractures occur in patients who do not fulfil this criterion. As BMD alone does not adequately predict fracture risk, additional methods supporting risk assessment are needed [3,4]. In 2012, the US Food and Drug Administration (FDA) adopted the Trabecular Bone Score (TBS) as another diagnostic method for osteoporosis. The aim of this study is to evaluate the use of TBS in clinical practice with particular attention to fracture risk assessment, differential diagnosis and assessment of treatment outcomes in patients suffering from primary or secondary osteoporosis. Literature analysis points to the increasing use of TBS in clinical practice. It has been found that, in different subjects with the same BMD, structural bone health is better differentiated using TBS. Additionally, it is valuable to use the DXA along with TBS analysis for improved prediction of fracture risk. Indirect assessment of bone structure with TBS is also helpful in assessing the effect of medications. In conclusion, TBS analysis is a valuable tool in the assessment of bone fracture risk and in the differential diagnosis of structural bone disorders in secondary osteoporosis.

Explicit expressions for the estimation of the elastic constants of lamellar bone as a function of the volumetric mineral content using a multi-scale approach

In this work, explicit expressions to estimate all the transversely isotropic elastic constants of lamellar bone as a function of the volumetric bone mineral density (BMD) are provided. The methodology presented is based on the direct homogenization procedure using the finite element method, the continuum approach based on the Hill bounds, the least-square method and the mean field technique. Firstly, a detailed description of the volumetric content of the different components of bone is provided. The parameters defined in this step are related to the volumetric BMD considering that bone mineralization process occurs at the smallest scale length of the bone tissue. Then, a thorough description provides the details of the numerical models and the assumptions adopted to estimate the elastic behaviour of the forward scale lengths. The results highlight the noticeable influence of the BMD on the elastic modulus of lamellar bone. Power law regressions fit the Young's moduli, shear stiffness moduli and Poisson ratios. In addition, the explicit expressions obtained are applied to the estimation of the elastic constants of cortical bone. At this scale length, a representative unit cell of cortical bone is analysed including the fibril orientation pattern given by Wagermaier et al. (Biointerphases 1:1-5, 2006) and the BMD distributions observed by Granke et al. (PLoS One 8:e58043, 2012) for the osteon. Results confirm that fibril orientation arrangement governs the anisotropic behaviour of cortical bone instead of the BMD distribution. The novel explicit expressions obtained in this work can be used for improving the accuracy of bone fracture risk assessment.

Multiscale modeling of bone fracture using cohesive finite elements

Bone is a hierarchical material that exhibit fracture mechanisms at multiple scales and will benefit from a multiscale evaluation approach for better fracture risk assessment. This study developed a cohesive finite element modeling approach that simulated bone fracture at micro- and macroscale. Simulation results showed that the microscale fracture toughening was most effective when the cement line had lower strength than the surrounding bone reducing the propensity to fracture at the macroscale. These results demonstrate the importance of cement line strength in controlling the fracture toughening mechanisms and the effect of microscale properties in the whole bone fracture risk assessment.

Osteoporosis following organ transplantation: pathogenesis, diagnosis and management

Organ transplantation has become popular for the management of various chronic illnesses. With the advent of modern immunosuppressive treatments, the longevity of transplant recipients has increased. Consequently, morbid complications such as osteoporosis and bone fractures are seen at an increasing frequency in this population. In most transplant recipients, bone mineral density (BMD) falls shortly after transplantation. However, bone fracture rate plateaus in all except for post-renal transplant patients. Although the underlying pathophysiologic mechanism for this difference is not fully understood, potential mechanisms for sustained bone loss in renal transplant recipients may be persistent phosphorus wasting and defective bone mineralization. Current treatment regimens are based on studies in a small numbers of subjects with BMD as the primary outcome. Although BMD is recognized as a gold standard in the assessment of bone fracture risk, to date, its association with bone fracture risk in the general post-transplant population is not robust. Therefore, randomized controlled trials with bone fracture as the primary end point are crucial. The development of noninvasive bone markers in distinguishing bone turnover and bone mineralization status is also pivotal since skeletal lesions are heterogeneous in various organ transplantations. The elucidation of these underlying skeletal lesions is necessary for the consideration of selective treatment in this population.