Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

ABSTRACT:

Background

Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion.

Results</

SUBMITTER: Zhao C 

PROVIDER: S-EPMC10710145 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature