Population-specific femur models: A step towards improved osteosynthetic biomechanical testing in orthopaedics

Background: Biomechanical testing using synthetic bone surrogates has become a standard method for evaluating osteosynthesis techniques. However, these surrogates often fail to account for population-specific variations in bone anatomy and mechanical properties, leading to limitations in predicting clinical outcomes. This study addresses this gap by developing and validating a population-specific synthetic femur model for older women of European ethnicity, incorporating variations in geometry and mechanics observed in this demographic. Methods: The femur model was developed using a statistical shaping algorithm and 3D models from women aged 75 to 85 years. Synthetic femora were fabricated using polyurethane, enriched with fillers and additives to mimic osteoporotic bone characteristics. Mechanical testing, including axial compression, four-point bending, and torsion, was performed on synthetic femora and were validated against human osteoporotic femora. Findings: The synthetic femora demonstrated comparable mechanical properties to human osteoporotic femora, particularly in flexural and torsional rigidity. Axial stiffness was slightly lower in the synthetic femora but remained within the range of literature values. Statistical analysis revealed significant differences between synthetic and human bones in certain parameters, highlighting the need for population-specific models. Interpretation: The developed synthetic femur model offers a promising solution for addressing the limitations of current bone surrogates in biomechanical testing. By incorporating population-specific characteristics these models provide a more accurate representation of human bone, improving the validity of biomechanical evaluations and potentially leading to more equitable treatment outcomes in orthopaedics. Further research is warranted to explore the applicability of these models across different populations and anatomical sites.