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Updated: Jan 11, 2026
A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
Published on: September 14, 2017
Parametrized statistical appearance and shape modelling strategy to predict proximal and diaphyseal femoral fractures
Özgür Cebeci1,2, Duane S Cronin3, Sara Checa2,4
1IAT Ingenieurgesellschaft für Automobiltechnik mbH, Berlin, Germany.
Introduction:
Femoral loading leading to a fracture is known to vary with anthropometry, and patient-specific finite element models have provided important insights into fracture prediction but are often very time consuming to generate. Additionally, existing parametric models do not simultaneously account for variations in both femur geometry and bone density distribution and remain limited to either the femoral shaft or the proximal femur. This inhibits their ability to predict fractures involving both the shaft and proximal regions.
Methods:
In the present study, a novel parametric femur modeling strategy was developed to create whole femur models based on stature, BMI, and age input, including density distribution and geometrical variations, for fracture loading predictions. A statistical shape and appearance femur model was developed based on an input set of CT scans of healthy female femurs (N = 18) between the ages of 50 and 70. Thereafter, multilinear regressions were used to relate principal components to the subject anthropometric characteristics and develop parametric models. The developed parametric models were evaluated using traditional patient-specific models for their potential to represent the influence of changing patient stature, BMI, and age on femoral fractures. Femoral fracture load in three-point bending, axial torsion, and lateral fall cases was predicted using the parametric as well as subject-specific femur models.
Results:
The developed parametric model was able to predict femoral fracture load variations due to changing anthropometry and age with an average difference of 4.85% compared with predictions using subject-specific models.
Discussion:
Therefore, this novel parametric femur model can predict fracture loading while directly incorporating the influence of changing patient anthropometry. In the future, the model could support the development of orthopedic devices tailored to specific patient anthropometries to help mitigate femoral fractures.
