Deep Learning Network-Tailored Microenvironment Matching of 4D Bioprinting Bioactive Scaffolds for Bone Regeneration

Area of Science:

• Biomaterials Science
• Regenerative Medicine
• Artificial Intelligence in Medicine

Background:

• Pathological microenvironments in aging, trauma, and disease impair bone fracture healing and lead to nonunion.
• Current bone repair therapies have limitations in adapting to diverse anatomical defects and clinical conditions.

Purpose of the Study:

• To develop a microenvironment-adaptive bioactive scaffold (MABS) using deep learning (DLN) and 4D printing for enhanced bone repair.
• To overcome the limitations of static bone implants by creating dynamically adaptive systems.

Main Methods:

• Fabrication of a MABS integrating bioactive glass and a shape-memory polymer (PgP) matrix.
• Optimization of scaffold design using a multilayer perceptron (MLP) neural network for nonlinear parameter-performance analysis.
• In vivo evaluation of scaffold performance in promoting osteogenesis and angiogenesis.

Main Results:

• The DLN-optimized scaffold demonstrated enhanced shape-morphing adaptability.
• Significant promotion of dense bone tissue formation and vascular network development was observed.
• The intelligent framework successfully integrated 4D printing dynamics, degradation kinetics, and biological responses.

Conclusions:

• The synergistic DLN and 4D printing approach offers a scalable and intelligent framework for precise bone repair.
• This novel paradigm transforms bone implants into dynamically adaptive systems, addressing deficiencies of current strategies.
• The MABS shows potential for treating complex bone fractures and nonunion in pathological conditions.