Resolving the multiscale design trade-off in bone tissue engineering: from generative design to digital twins

Area of Science:

• Biomaterials Science
• Regenerative Medicine
• Artificial Intelligence in Healthcare

Background:

• Bone tissue engineering faces a critical design challenge: balancing mechanical strength with vascularization for clinical success.
• Current methods address scaffold properties independently, failing to optimize for competing requirements.

Purpose of the Study:

• To present an AI-Driven Holistic Intelligence framework for integrated design and prediction in bone tissue engineering.
• To overcome the limitations of conventional approaches in navigating multiscale design trade-offs.

Main Methods:

• Developed a unified workflow integrating generative inverse design, digital twin-based predictive maturation, gradient scaffold optimization, and adaptive manufacturing.
• Implemented a co-optimization strategy for stiffness and vascular perfusion within a closed-loop system.

Main Results:

• The framework enables simultaneous optimization of mechanical strength and vascular perfusion, expanding the design space.
• Demonstrated a novel approach to resolve, rather than merely navigate, the empirical trade-off in scaffold design.

Conclusions:

• The AI-Driven Holistic Intelligence framework offers a pathway to simultaneously enhance mechanical properties and vascularization in bone scaffolds.
• Addressing data standardization, algorithm interpretability, and regulatory alignment is crucial for clinical translation of this autonomous regeneration approach.