Machine Learning in Tissue Engineering.

Area of Science:

• Biomedical Engineering
• Tissue Engineering
• Computational Biology

Background:

• Machine learning (ML) and artificial intelligence (AI) are transforming scientific discovery and clinical practice.
• ML applications are expanding into diverse areas of tissue engineering research.
• Existing research highlights the potential of ML in biomaterial design, scaffold fabrication, and cell/tissue modeling.

Purpose of the Study:

• To review novel methodologies and emerging strategies in ML-driven tissue engineering.
• To highlight areas of potential growth in this rapidly evolving research field.
• To showcase how ML enhances the analysis of factors influencing tissue regeneration.

Main Methods:

• Review of current literature on ML applications in tissue engineering.
• Analysis of ML-empowered strategies such as machine-optimized polymer synthesis and predictive modeling.
• Examination of deep learning for spatialized cell phenotypes and tissue composition.

Main Results:

• ML enables machine-optimized biomaterial design and predictive modeling of scaffold fabrication.
• Deep learning facilitates complex analyses of structure-function relationships and cell phenotypes.
• ML facilitates multivariate analyses of biological, chemical, and physical factors in tissue regeneration.

Conclusions:

• ML is driving new paradigms in tissue engineering research.
• The integration of ML allows for more sophisticated analyses of tissue regenerative outcomes.
• Significant growth potential exists for ML applications in advancing tissue engineering.