Machine learning in tissue engineering: a comprehensive review of applications, challenges, and prospects.

Area of Science:

• Biomaterials Science
• Regenerative Medicine
• Computational Biology

Background:

• Tissue engineering aims to repair or replace tissues/organs using cell biology and materials science.
• Current limitations include long production times, high costs, and unpredictable outcomes.
• Artificial intelligence (AI), machine learning (ML), and deep learning (DL) offer potential solutions.

Purpose of the Study:

• To explore AI, ML, and DL applications in tissue engineering.
• To focus on optimizing biomaterials, scaffold design, tissue regeneration, and 3D bioprinting.
• To review current advancements, challenges, and future directions.

Main Methods:

• Review of recent literature on AI, ML, and DL in tissue engineering.
• Analysis of AI applications in biomaterial property prediction and optimization.
• Examination of AI's role in scaffold design, tissue regeneration, and bioprinting.

Main Results:

• AI/ML/DL can optimize biomaterial selection and scaffold fabrication.
• These technologies aid in predicting tissue growth and improving transplantation.
• Successful applications are emerging in areas like 3D bioprinting.

Conclusions:

• The integration of AI, ML, and DL holds significant promise for tissue engineering.
• Challenges related to data quality, interpretability, and standardization need addressing.
• Further research is crucial to fully realize the potential of AI in regenerative medicine.