Abstract:
Skeletal fractures, particularly those linked to bone mass loss, pose a significant clinical challenge and economic burden, leading to substantial morbidity and mortality in the aging population. While clinical imaging methods provide bone mass measurements, their correlation with actual bone strength is only moderate. In contrast, biomechanical engineering models derived from clinical image data have demonstrated significantly greater accuracy in predicting bone strength. However, current image-based finite element (FE) models are often time-consuming to construct and lack parametric descriptions. This study aimed to address these limitations by developing a parametric proximal femur FE model founded on a statistical shape and density model (SSDM), meticulously derived from clinical image data. The methodology involved creating a compact representation where a limited number of independent SSDM parameters effectively described the complex shape and bone density distribution observed in a set of cadaver femurs. This approach successfully captured the inherent variability that influences proximal femur FE strength predictions. The SSDM demonstrated high accuracy in reconstructing individual femurs from the training set. Importantly, a three-dimensional FE model of an "unknown" femur was accurately reconstructed from the SSDM, achieving an average spatial error of 0.016 mm and an average bone density error of 0.037 g/cm3. This parametric biomechanical modeling procedure significantly reduces the time required for model construction while maintaining predictive accuracy, enabling investigations into how specific variations in bone geometry and density affect bone strength, thus aiding in identifying individuals at high risk of fracture. The study concludes that this physics-based predictive finite element modeling approach offers a substantial advantage over purely statistical correlation methods for assessing bone strength in a population.
