2024/01/04 | Research | Biomechanics
The Musculoskeletal Biomechanics group has recently presented a multimodal framework for human compact bone characterization at the microscale, including the first high-throughput micropillar compressions of human cortical bone. The proposed bone fracture risk classification algorithm together with the output dataset of bone tissue properties can be used for the future comparison of existing methods to evaluate bone quality and better understand the mechanisms through which bone tissue is affected by aging or disease.
For the study in collaboration with the Department of Orthopedic Surgery, Inselspital, Empa and the Institute for Applied Laser, Photonics and Surface Technologies (ALPS), the team analyzed femoral neck samples from 42 patients together with anonymous clinical information about age, sex and primary diagnosis (coxarthrosis or hip fracture). The femoral neck cortex from the inferomedial region was analyzed in a site-matched manner using a combination of micromechanical testing (nanoindentation, micropillar compression) together with micro-CT and quantitative polarized Raman spectroscopy for both morphological and compositional characterization.
Mechanical properties, as well as the sample-level mineral density, were constant over age. Only compositional properties demonstrate weak dependence on patient age. The patient's sex and diagnosis did not seem to influence investigated bone properties. The output database is the first to integrate the experimentally assessed microscale yield properties, local tissue composition and morphology with the available patient clinical information. The final dataset was used for bone fracture risk prediction in-silico.
Due to the low number of samples, further studies to build a universal fracture prediction algorithm are anticipated with the higher number of patients (N > 200).
Link to the study