Page 68 - IJB-9-4
P. 68
International Journal of Bioprinting Machine learning and 3D bioprinting
14. Jing L, Sun J, Liu H, et al., 2021, Using plant proteins to 26. Conev A, Litsa EE, Perez MR, et al., 2020, Machine learning-
develop composite scaffolds for cell culture applications. Int guided three-dimensional printing of tissue engineering
J Bioprint, 7(1):66–77. scaffolds. Tissue Eng Part A, 26(23-24):1359–1368.
https://doi.org/10.18063/ijb.v7i1.298 https://doi.org/10.1089/ten.tea.2020.0191
15. Sun J, Jing L, Fan X, et al., 2019, Electrohydrodynamic 27. Fu Z, Angeline V, Sun W, 2021, Evaluation of printing
printing process monitoring by microscopic image parameters on 3D extrusion printing of pluronic hydrogels
identification. Int J Bioprint, 5(1):1–9. and machine learning guided parameter recommendation.
https://doi.org/10.18063/ijb.v5i1.164 Int J Bioprint, 7(4):179–189.
16. Sun J, Jing L, Liu H, et al., 2020, Generating nanotopography https://doi.org/10.18063/ijb.v7i4.434
on PCL microfiber surface for better cell-scaffold 28. Ball AK, Das R, Roy SS, et al., 2020, Modeling of EHD
interactions. Proc Manuf, 48:619–624. inkjet printing performance using soft computing-based
https://doi.org/10.1016/j.promfg.2020.05.090 approaches. Soft Comput, 24(1):571–589.
17. An J, Chua CK, Mironov V, 2021, Application of machine https://doi.org/10.1007/s00500-019-04202-0
learning in 3D bioprinting: Focus on development of big
data and digital twin. Int J Bioprint, 7(1):1–6. 29. Huang J, Segura LJ, Wang T, et al., 2020, Unsupervised learning
for the droplet evolution prediction and process dynamics
https://doi.org/10.18063/ijb.v7i1.342 understanding in inkjet printing. Addit Manuf, 35:101197.
18. Sun J, Hong G, Rahman M, et al., 2005, Improved https://doi.org/10.1016/j.addma.2020.101197
performance evaluation of tool condition identification
by manufacturing loss consideration. Int J Prod Res, 30. Ruberu K, Senadeera M, Rana S, et al., 2021, Coupling machine
43(6):1185–1204. learning with 3D bioprinting to fast track optimisation of
extrusion printing. Appl Mater Today, 22:100914.
https://doi.org/10.1080/00207540412331299701
https://doi.org/10.1016/j.apmt.2020.100914
19. Sun J, Yao K, Huang K, et al., 2022, Machine learning
applications in scaffold based bioprinting. Mater Today 31. Das R, Ball AK, Roy SS, 2018, Optimization of E-jet based
Proc, 70 (2022):17–23. micro-manufacturing process using desirability function
analysis, in Industry Interactive Innovations in Science,
20. Jie S, Hong GS, Rahman M, et al., 2002, Feature extraction and Engineering and Technology, Springer, 477–484.
selection in tool condition monitoring system, in Australian
Joint Conference on Artificial Intelligence, 487–497. 32. Lee J, Oh SJ, An SH, et al., 2020, Machine learning-based design
strategy for 3D printable bioink: Elastic modulus and yield
21. MathWorks, 2022, Support vector machine classification stress determine printability. Biofabrication, 12(3):035018.
[EB/OL].
https://doi.org/10.1088/1758-5090/ab8707
https://www.mathworks.com/help/stats/support-vector-
machine-classification.html (Accessed November 8, 2022) 33. Bone JM, Childs CM, Menon A, et al., 2020, Hierarchical
machine learning for high-fidelity 3D printed biopolymers.
22. Yu C, Jiang J, 2020, A perspective on using machine learning
in 3D bioprinting. Int J Bioprint, 6(1):4–11. ACS Biomater Sci Eng, 6(12):7021–7031.
https://doi.org/10.18063/ijb.v6i1.253 https://doi.org/10.1021/acsbiomaterials.0c00755
23. Shin J, Lee Y, Li Z, et al., 2022, Optimized 3D bioprinting 34. Tian S, Stevens R, McInnes BT, et al., 2021, Machine assisted
technology based on machine learning: A review of recent experimentation of extrusion-based bioprinting systems.
trends and advances. Micromachines, 13(3):363. Micromachines, 12(7):780.
https://doi.org/10.3390/mi13030363 https://doi.org/10.3390/mi12070780
24. Kalantary S, Jahani A, Jahani R, 2020, MLR and Ann 35. Tourlomousis F, Jia C, Karydis T, et al., 2019, Machine
approaches for prediction of synthetic/natural nanofibers learning metrology of cell confinement in melt electrowritten
diameter in the environmental and medical applications. Sci three-dimensional biomaterial substrates. Microsyst
Rep, 10(1):1–10. Nanoeng, 5(1):1–19.
https://doi.org/10.1038/s41598-020-65121-x https://doi.org/10.1088/1758-5090/ab8707
25. Jin Z, Zhang Z, Shao X, et al., 2021, Monitoring anomalies 36. Yao K, Huang K, Sun J, et al., 2021, Scaffold-A549:
in 3D bioprinting with deep neural networks. ACS Biomater A benchmark 3D fluorescence image dataset for unsupervised
Sci Eng. nuclei segmentation. Cognit Comput, 13(6):1603–1608.
https://doi.org/10.1021/acsbiomaterials.0c01761 https://doi.org/10.1007/s12559-021-09944-4
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