Accurate numerical and physical models of trabecular bone, correctly representing its complexity and variability, could be highly advantageous in the development of e.g. new bone-anchored implants due to the limited availability of real bone. Several Voronoi tessellation-based porous models have been reported in the literature, attempting to mimic the trabecular bone. However, these models have been limited to lattice rod-like structures, which are only structurally representative of very high-porosity trabecular bone. The objective of this study was to provide an improved model, more representative of trabecular bone of different porosity. Boolean operations were utilized to merge scaled Voronoi cells, thereby introducing different structural patterns, controlling porosity and to some extent anisotropy. The mechanical properties of the structures were evaluated using analytical estimations, numerical simulations, and experimental compression tests of 3D-printed versions of the structures. The capacity of the developed models to represent trabecular bone was assessed by comparing some key geometric features with trabecular bone characterized in previous studies. The models gave the possibility to provide pore interconnectivity at relatively low porosities as well as both plate- and rod-like structures. The mechanical properties of the generated models were predictable with numerical simulations as well as an analytical approach. The permeability was found to be better than Sawbones at the same porosity. The models also showed the capability of matching e.g. some vertebral structures for key geometric features. An improved numerical model for mimicking trabecular bone structures was successfully developed using Voronoi tessellation and Boolean operations. This is expected to benefit both computational and experimental studies by providing a more diverse and representative structure of trabecular bone.

Silicon nitride is utilized clinically as a bioceramic for spinal fusion cages, owing to its high strength, osteoconductivity, and antibacterial effects. Nevertheless, silicon nitride exhibits suboptimal damping properties, a critical factor in mitigating traumatic bone injuries and fractures. In fact, there is a scarcity of spinal implants that simultaneously demonstrate proficient damping performance and support osteogenesis. In our study, we fabricated a novel sodium alginate-silicon nitride/poly(vinyl alcohol) (SA-SiN/PVA) composite scaffold, enabling enhanced energy absorption and rapid elastic recovery under quasi-static and impact loading scenarios. Furthermore, the study demonstrated that the incorporation of physical and chemical cross-linking significantly improved stiffness and recoverable energy dissipation. Concerning the interaction between cells and materials, our findings suggest that the addition of silicon nitride stimulated osteogenic differentiation while inhibiting Staphylococcus aureus growth. Collectively, the amalgamation of ceramics and tough hydrogels facilitates the development of advanced composites for spinal implants, manifesting superior damping, osteogenic potential, and antibacterial properties. This approach holds broader implications for applications in bone tissue engineering.

Silicon nitride (Si₃N₄) is a promising biomaterial, currently used in spinal fusion implants. Such implants should result in high vertebral union rates without major complications. However, pseudarthrosis remains an important complication that could lead to a need for implant replacement. Making silicon nitride implants more bioactive could lead to higher fusion rates, and reduce the incidence of pseudarthrosis. In this study, it was hypothesized that creating a highly negatively charged Si₃N₄ surface would enhance its bioactivity without affecting the antibacterial nature of the material. To this end, samples were thermally, chemically, and thermochemically treated. Apatite formation was examined for a 21-day immersion period as an in-vitro estimate of bioactivity. Staphylococcus aureus bacteria were inoculated on the surface of the samples, and their viability was investigated. It was found that the thermochemically and chemically treated samples exhibited enhanced bioactivity, as demonstrated by the increased spontaneous formation of apatite on their surface. All modified samples showed a reduction in the bacterial population; however, no statistically significant differences were noticed between groups. This study successfully demonstrated a simple method to improve the in vitro bioactivity of Si₃N₄ implants while maintaining the bacteriostatic properties.

The accurate evaluation of bone mechanical properties is essential for predicting fracture risk based on clinical computed tomography (CT) images. However, blurring and noise in clinical CT images can compromise the accuracy of these predictions, leading to incorrect diagnoses. Although previous studies have explored enhancing trabecular bone CT images to super-resolution (SR), none of these studies have examined the possibility of using clinical CT images from different instruments, typically of lower resolution, as a basis for analysis. Additionally, previous studies rely on 2D SR images, which may not be sufficient for accurate mechanical property evaluation, due to the complex nature of the 3D trabecular bone structures. The objective of this study was to address these limitations. A workflow was developed that utilizes convolutional neural networks to generate SR 3D models across different clinical CT instruments. The morphological and finite-element-derived mechanical properties of these SR models were compared with ground truth models obtained from micro-CT scans. A significant improvement in analysis accuracy was demonstrated, where the new SR models increased the accuracy by up to 700 % compared with the low-resolution data, i.e. clinical CT images. Additionally, we found that the mixture of different CT image datasets may improve the SR model performance. SR images, generated by convolutional neural networks, outperformed clinical CT images in the determination of morphological and mechanical properties. The developed workflow could be implemented for fracture risk prediction, potentially leading to improved diagnoses and subsequent clinical decision making.

Screw-bone construct failures are a true challenge in orthopaedic implant fixation, particularly in poor quality bone. Whilst augmentation with bone cement can improve the primary stability of screws, there are cements, e.g. PMMA, that may impede blood flow and nutrients and hamper bone remodelling. In this study, soft, non-setting biomaterials based on Hyalectin gels and hydroxyapatite (HA) particles with different morphological parameters were evaluated as potential augmentation materials, using a lapine ex vivo bone model. The pull-out force, stiffness, and work to fracture were considered in evaluating screw attachment. The pull-out force of constructs reinforced with Hyalectin containing irregularly shaped nano-HA and spherically shaped micro-HA particles were found to be significantly higher than the control group (no augmentation material). The pull-out stiffness increased for the micro-HA particles and the work to fracture increased for the irregular nano-HA particles. However, there were no significant augmentation effect found for the spherical shaped nano-HA particles. In conclusion, injectable Hyalectin gel loaded with hydroxyapatite particles was found to have a potentially positive effect on the primary stability of screws in trabecular bone, depending on the HA particle shape and size.

Screw implant stability in bone is crucial to the success of many orthopaedic procedures, yet the relationship between screw design parameters and specific bone characteristics remains underexplored. This study aims to optimize screw designs to enhance primary stability by leveraging subject-specific bone data and advanced surrogate modelling techniques. In this study, 2880 screw pull-out simulations were conducted to assess primary screw stability by analysing pull-out stiffness and strength. The resulting dataset was used to develop surrogate models using multiple linear regression, random forest, and neural networks (NN). An optimization process was then applied to find optimal screw designs for 80 distinct trabecular bone specimens, in terms of inner diameter, pitch, and thread angle. The models, trained with various input parameters, including bone morphological parameters and computed tomography images, promisingly predicted the results of the simulations. The prediction errors varied by model type, with multiple linear regression yielding approximately 12 % error, while non-linear machine learning models achieved lower errors, ranging between 2-6 %. The series of subsequent optimization tasks provided optimized screw designs showing statistically significant improvements in pull-out stiffness and strength compared to the average screw designs (approximately 16 and 14 %, respectively). This even though our study focused only on screw design parameters that generally have a smaller impact on stability compared to factors such as screw outer diameter and insertion depth. Multiple linear regression models were found to be insufficient for generating optimized screw configurations, and more complex surrogate models, such as NN, are needed. It could be concluded that different trabecular bone morphologies can benefit from distinct optimal screw designs. The insights gained from this study could have implications for the development of patient-specific orthopaedic treatments.