Capturing the Multiscale Nature of Bone Behavior: Classical, Data-Driven and Hybrid Techniques

Area of Science:

• Biomechanics
• Computational Biology
• Materials Science

Background:

• Bone architecture exhibits complex hierarchical organization across multiple spatiotemporal scales.
• Precisely capturing bone's mechanical behavior requires advanced computational modeling techniques.
• Existing methods face challenges in integrating biological data and balancing efficiency with interpretability.

Purpose of the Study:

• To review recent trends in multiscale bone modeling using classical and data-driven computational frameworks.
• To assess the versatility, scalability, and biological data integration capabilities of these approaches.
• To provide actionable guidance for selecting appropriate modeling techniques based on application context.

Main Methods:

• Comparative assessment of classical computational frameworks for multiscale bone modeling.
• Evaluation of data-driven frameworks for capturing bone's hierarchical mechanical behavior.
• Analysis of hybrid techniques and their synergy for robust and generalizable modeling.

Main Results:

• Both classical and data-driven frameworks offer distinct advantages and limitations in multiscale bone modeling.
• Hybrid techniques show promise for enhanced robustness and generalizability in biomechanical modeling.
• A hierarchical decision matrix is proposed to guide technique selection based on data availability and objectives.

Conclusions:

• Selecting the appropriate multiscale modeling technique is crucial for accurate bone biomechanics.
• Hybrid approaches represent a promising future direction for bone architecture analysis.
• This review serves as a practical reference for advancing multiscale bone biomechanics research.