ArticleScientific reports2026
Statistical shape modeling of the proximal femur bone for hip implant design optimization.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
The femur is the major load-bearing skeletal component in the human body, and its proximal end is prone to either osteoporosis or high-energy injury-related fractures. When a fracture occurs, prosthetic implants, depending on the type and level of injury, are used to restore the normal biomechanics of the bone. Fracture fixation and the subsequent bone healing process depend on several factors, including the anatomical fitting quality of the prosthetic implant utilized. However, the standard commercially available prosthetic implants are generic, and commonly implants are designed and validated based on a limited set of cadaver bones, which may result in the incompatibility of implants to all patient groups. Utilization of an incompatible implant for patients may cause implant-related complications. In recent years, generic implants have been optimized for a specific population group on the basis of anatomical data obtained from the target population to improve the fitting quality of implants to the intended anatomical region. This study aimed to develop a statistical shape model (SSM) of the proximal femur bone and to carry out morphometric analysis of the femoral neck-shaft angle (NSA). The shape modelling process followed sequential steps, including image segmentation and 3D surface reconstruction, manual annotation of anatomical landmarks, establishment of surface correspondence across datasets, and model building via principal component analysis. Additionally, femoral NSA measurements are implemented. The quality of the SSM was validated through shape quality metrics: compactness, specificity, and generality. The validation result revealed that the first eleven principal components of the SSM represented 92.85% of the total variance in the model. Similarly, the specificity and generalizability of the model were approximately 1.33 mm and 0.83 mm, respectively.
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