In vivo validation of a computational bone adaptation model using open-loop control and time-lapsed micro-computed...

Area of Science:

• Biomechanical Engineering
• Computational Biology
• Orthopedic Research

Background:

• Cyclic mechanical loading is known to increase trabecular bone mass, primarily through increased thickness.
• Existing computational models aim to predict bone's response to mechanical stimuli.

Purpose of the Study:

• To test if an open-loop computational algorithm could reliably predict trabecular bone adaptation to cyclic mechanical loading.
• To validate an in silico model using in vivo micro-computed tomography data from mice.

Main Methods:

• An in silico thickening algorithm with open-loop control was developed.
• Time-lapsed in vivo micro-computed tomography scans of mice subjected to cyclic loading were acquired.
• In silico predictions were compared against experimental data over a four-week period.

Main Results:

• The computational model achieved a maximum prediction error of 2.4% for bone volume fraction and 5.4% for other morphometric indices.
• The model accurately predicted global changes in bone structure but did not replicate localized bone formation and resorption patterns.
• In silico simulations showed homogeneous bone deposition, contrasting with experimental observations of varied thickening and resorption.

Conclusions:

• The proposed computational algorithm effectively predicts overall changes in bone volume fraction and global structural parameters.
• The model's inability to capture local bone remodeling highlights the need for more sophisticated feedback mechanisms in simulations.
• Validation with in vivo experimental data, including local comparisons, is crucial for refining computational models of bone adaptation.