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International Journal of Bioprinting Machine learning and 3D bioprinting

Both the traditional ML and DL methods can empower 2. Placone JK, Engler AJ, 2018, Recent advances in extrusion‐
bioprinting by manipulating and optimizing micro/ based 3D printing for biomedical applications. Adv Healthc
nanostructures, materials, and printing parameters. Mater, 7(8):1701161.
This capability, when applied to bioprinted constructs, https://doi.org/10.1002/adhm.201701161
can generate more advanced concepts, enhance their 3. Papaioannou TG, Manolesou D, Dimakakos E, et al., 2019,
biological and mechanical performance, and prompt 3D bioprinting methods and techniques: Applications
effective cell–microenvironment interactions. These on artificial blood vessel fabrication. Acta Cardiol Sinica,
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compositions as well as specific micro/nanostructures https://doi.org/10.6515/ACS.201905_35(3).20181115A
would establish a solid foundation for developing organoid
and tumoroid models from a technical perspective. It is no 4. He J, Zhang B, Li Z, et al., 2020, High-resolution
doubt that ML methods would expand their applications to electrohydrodynamic bioprinting: A new biofabrication
facilitate diverse printing scenarios and application topics strategy for biomimetic micro/nanoscale architectures and
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in the near future.
https://doi.org/10.1088/1758-5090/aba1fa
Acknowledgments 5. Ng WL, Chan A, Ong YS, et al., 2020, Deep learning for
fabrication and maturation of 3D bioprinted tissues and
None. organs. Virtual Phys Prototyp, 15(3):340–358.
Funding https://doi.org/10.1080/17452759.2020.1771741
6. Regenhu, 2022, The R-GEN 100 bioprinter [EB/OL].
This work was financially supported by Xi’an Jiaotong-
Liverpool University’s Key Program Special Fund under https://www.regenhu.com/3dbioprinting-solutions/r-gen-
Grant KSF-E-37. 100-3dbioprinter (Accessed November 8, 2022)
7. 3dsman, 2022, EnvisionTEC: 3D-Bioplotter [EB/OL].
Conflict of interest https://3dsman.com/envisiontec-3d-bioplotter (Accessed

The authors declare no conflicts of interest. November 8, 2022)
8. Ozbolat IT, Hospodiuk M, 2016, Current advances and
Author contributios future perspectives in extrusion-based bioprinting.
Conceptualization: Kaizhu Huang, Dejian Huang Biomaterials, 76:321–343.
Investigation: Jia An, Linzhi Jing https://doi.org/10.1016/j.biomaterials.2015.10.076
Supervision: Kaizhu Huang, Dejian Huang 9. Ning L, Chen X, 2017, A brief review of extrusion‐based
Writing – original draft: Jie Sun, Kai Yao tissue scaffold bio‐printing. Biotechnol J, 12(8):1600671.
Writing – editing & review: Jia An, Linzhi Jing https://doi.org/10.1002/biot.201600671
All authors read and approved the manuscript.
10. Brown TD, Dalton PD, Hutmacher DW, 2011, Direct writing
Ethics approval and consent to participate by way of melt electrospinning. Adv Mater, 23(47):5651–
5657.
Not applicable. https://doi.org/10.1002/adma.201103482

Consent for publication 11. Wu Y, Fuh J, Wong Y, et al., 2015, Fabrication of 3D scaffolds
via E-jet printing for tendon tissue repair, in International
Not applicable. Manufacturing Science and Engineering Conference, 56833,
V002T03A005.
Availability of data 12. Zhang B, Seong B, Nguyen V, et al., 2016, 3D printing
of high-resolution PLA-based structures by hybrid
Not applicable.
electrohydrodynamic and fused deposition modeling
techniques. J Micromech Microeng, 26(2):025015.
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Volume 9 Issue 4 (2023) 59 https://doi.org/10.18063/ijb.717