Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jan 29;7(1):342.
doi: 10.18063/ijb.v7i1.342. eCollection 2021.

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

Jia An  1 , Chee Kai Chua  2 , Vladimir Mironov  3
Affiliations

Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

Jia An et al. Int J Bioprint. .

Abstract

The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.

Keywords: 3D bioprinting; Big data; Complexity; Digital twin; Machine learning.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Example methods in machine learning.
Figure 2
Figure 2
A vision for future bioprinting.
See this image and copyright information in PMC

References

    1. Yu C, Jiang J. A Perspective on Using Machine Learning in 3D Bioprinting. Int J Bioprinting. 2020;6:95. https://doi.org/10.18063/ijb.v6i1.253. - PMC - PubMed
    1. Ng WL, Chan A, Ong YS, et al. Deep Learning for Fabrication and Maturation of 3D Bioprinted Tissues and Organs. Virtual Phys Prototyp. 2020;15:340–58.
    1. Meng L, McWilliams B, Jarosinski W, et al. Machine Learning in Additive Manufacturing:A Review. JOM. 2020;72:1–15.
    1. Goh G, Sing S, Yeong W. A Review on Machine Learning in 3D Printing:Applications, Potential, and Challenges. Artif Intell Rev. 2020;54:63–94. https://doi.org/10.1007/s10462-020-09876-9.
    1. Hamid OA, Eltaher HM, Sottile V, et al. 3D Bioprinting of a Stem Cell-laden, Multi-material Tubular Composite:An Approach for Spinal Cord Repair. Mater Sci Eng C. 2020;2020:111707. https://doi.org/10.1016/j.msec.2020.111707. - PubMed

LinkOut - more resources