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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 boosts three-dimensional bioprinting.

Hongwei Ning¹, Teng Zhou², Sang Woo Joo³

¹College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, Anhui, China.

International Journal of Bioprinting

|June 16, 2023

Summary

This summary is machine-generated.

Machine learning enhances three-dimensional (3D) bioprinting by optimizing bioinks and printing parameters. This integration improves accuracy and reduces cell damage, advancing tissue engineering applications.

Keywords:

Additive manufacturing Bioprinting Ensemble learning K-nearest neighbor Long short-term memory

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Area of Science:

Biotechnology
Materials Science
Computer Science

Background:

Three-dimensional (3D) bioprinting is an additive manufacturing technology for creating biological structures.
Current challenges include selecting suitable bioinks and improving printing accuracy.
Cell damage and mortality are significant concerns in the bioprinting process.

Purpose of the Study:

To review the integration of machine learning (ML) with 3D bioprinting.
To explore ML's role in addressing current bioprinting challenges.
To summarize recent advancements in ML-driven bioink development, parameter optimization, and defect detection.

Main Methods:

Review of machine learning algorithms applicable to bioprinting.
Analysis of ML's role in additive manufacturing.
Synthesis of research on ML applications in 3D bioprinting for bioink generation, parameter optimization, and defect detection.

Main Results:

Machine learning algorithms offer predictive capabilities for behavior prediction and model exploration.
ML aids in identifying efficient bioinks and optimizing printing parameters.
ML facilitates the detection of defects during the 3D bioprinting process.

Conclusions:

The combination of ML and 3D bioprinting shows significant promise for advancing tissue engineering.
ML can overcome key limitations in bioink selection, printing accuracy, and process monitoring.
Further research in this interdisciplinary field is expected to yield innovative solutions.