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Viability of Bioprinted Cellular Constructs Using a Three Dispenser Cartesian Printer

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Published on: September 22, 2015

Machine learning and 3D bioprinting.

Jie Sun¹, Kai Yao^1,2, Jia An^3,4

¹School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China.

International Journal of Bioprinting

|June 16, 2023

Summary

Machine learning (ML) enhances bioprinting by optimizing processes and materials for better tissue constructs. This review analyzes ML applications, highlighting advancements in printing stability, design, and cell performance for future innovations.

Keywords:

Biomaterials Bioprinted constructs Bioprinting Deep learning Machine learning

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Area of Science:

Biomaterials Engineering
Biotechnology
Computational Biology

Background:

Bioprinting enables fabrication of biomimetic architectures and living tissue constructs.
Machine learning (ML) offers potential to optimize bioprinting processes, materials, and construct performance.
A comprehensive analysis of ML applications in bioprinting is needed to guide future development.

Purpose of the Study:

To collate, analyze, and categorize ML applications in bioprinting.
To summarize the impact of ML on bioprinting processes and construct performance.
To identify future directions for ML in bioprinting technology and construct design.

Main Methods:

Systematic review of published articles on ML in bioprinting.
Categorization of ML approaches (traditional ML and deep learning).
Analysis of ML applications in optimizing printing process, material properties, and construct performance.

Main Results:

Both traditional ML and deep learning (DL) are applied to optimize bioprinting.
ML improves printing stability, precision (e.g., layer stacking), and material properties.
ML enhances bioprinted construct design and cell performance.

Conclusions:

ML significantly advances bioprinting, leading to more stable and reliable processes.
ML integration improves the quality and performance of bioprinted constructs.
Developing integrated process-material-performance models is crucial for revolutionizing bioprinting.