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. 2020 Jan 24;6(1):253.
doi: 10.18063/ijb.v6i1.253. eCollection 2020.

A Perspective on Using Machine Learning in 3D Bioprinting

Chunling Yu  1 , Jingchao Jiang  2
Affiliations

A Perspective on Using Machine Learning in 3D Bioprinting

Chunling Yu et al. Int J Bioprint. .

Erratum in

  • ERRATUM.
    [No authors listed] [No authors listed] Int J Bioprint. 2020 Sep 17;6(4):309. doi: 10.18063/ijb.v6i4.309. eCollection 2020. Int J Bioprint. 2020. PMID: 33102924 Free PMC article.

Abstract

Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printing processes, such as process optimization, dimensional accuracy analysis, manufacturing defect detection, and material property prediction. However, few studies have been found to use machine learning in 3D bioprinting processes. In this paper, related machine learning methods used in 3D printing are briefly reviewed and a perspective on how machine learning can also benefit 3D bioprinting is discussed. We believe that machine learning can significantly affect the future development of 3D bioprinting and hope this paper can inspire some ideas on how machine learning can be used to improve 3D bioprinting.

Keywords: 3D printing; Bioprinting; Machine learning.

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Figures

Figure 1
Figure 1
Typical three-dimensional printing process (a) and extrusion-based bioprinting methods (b).
Figure 2
Figure 2
A typical machine learning process.
Figure 3
Figure 3
Example neural network for process optimization in three-dimensional bioprinting.
Figure 4
Figure 4
An example of using machine learning (convolutional neural network) in three-dimensional bioprinting.
Figure 5
Figure 5
An example of using machine learning to design scaffolds for three-dimensional bioprinting.
See this image and copyright information in PMC

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