Artificial intelligence for design strategies of tissue engineering materials

Area of Science:

• Biomaterials Science
• Regenerative Medicine
• Artificial Intelligence in Medicine

Background:

• Tissue engineering materials design and performance prediction are complex challenges.
• Traditional methods struggle with the vast and intricate biological data involved.
• The need for advanced computational tools is critical for progress in regenerative medicine.

Purpose of the Study:

• To systematically review machine learning (ML) and deep learning (DL) based design methods for tissue engineering materials.
• To examine the applications, advantages, and challenges of AI in tissue engineering.
• To provide insights into future directions for AI-driven regenerative medicine.

Main Methods:

• Systematic literature review of ML- and DL-based approaches in tissue engineering.
• Analysis of AI algorithm applications in biomaterial data analysis.
• Evaluation of AI's role in optimizing material properties and predicting biological interactions.

Main Results:

• ML effectively optimizes mechanical properties, biocompatibility, and structural design of tissue engineering materials.
• DL excels at analyzing complex biological data, including material-cell-protein interactions, and promoting tissue regeneration.
• AI significantly enhances the prediction of material-cell interactions and material performance.

Conclusions:

• AI, particularly ML and DL, offers powerful tools for advancing tissue engineering material design and application.
• These computational methods are crucial for overcoming current limitations in regenerative medicine.
• Future research should focus on further integrating AI for more sophisticated biomaterial development and clinical translation.