Computational load estimation of the femur

Area of Science:

• Biomechanics
• Computational Biology
• Medical Imaging

Background:

• Bone density and mechanical properties are influenced by loading.
• Measuring in-vivo musculoskeletal loads non-invasively is challenging.
• Existing bone tissue adaptation theories link loading to density distribution.

Purpose of the Study:

• To investigate the prediction of musculoskeletal loads from bone density distributions.
• To solve the inverse problem of estimating loading parameters using bone adaptation models.
• To apply Artificial Neural Networks (ANNs) for this estimation.

Main Methods:

• Generated a dataset by solving the forward bone tissue adaptation model.
• Trained an ANN using this dataset for a proximal femur's CT-measured density distribution.
• Added Gaussian noise to density data to enhance ANN robustness.

Main Results:

• The ANN successfully estimated loading parameters.
• Predicted loading parameters resulted in density distributions closely matching measured data.
• The technique demonstrated robustness against deviations in density measurements.

Conclusions:

• ANNs can effectively solve the inverse problem of predicting musculoskeletal loads from bone density.
• This approach offers a promising non-invasive method for estimating bone loading.
• Findings contribute to understanding bone tissue adaptation and mechanical properties.