AccScience Publishing / IJB / Volume 9 / Issue 4 / DOI: 10.18063/ijb.717
Cite this article
105
Download
1324
Views
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
RESEARCH ARTICLE

Machine learning and 3D bioprinting

Jie Sun1* Kai Yao1,2 Jia An3,4 Linzhi Jing5 Kaizhu Huang6* Dejian Huang7
Show Less
1 School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China
2 School of Engineering, University of Liverpool, Liverpool, UK
3 Singapore Centre for 3D Printing, Nanyang Technological University, Singapore
4 Centre for Healthcare Education, Entrepreneurship and Research at SUTD University of Technology and Design, Singapore
5 National University of Singapore Suzhou Research Institute, Suzhou, China
6 Data Science Research Centre, Duke Kunshan University, Kunshan, China
7 National University of Singapore, Singapore
(This article belongs to the Special Issue Related to 3D printing technology and materials)
© Invalid date by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

With the growing number of biomaterials and printing technologies, bioprinting has brought about tremendous potential to fabricate biomimetic architectures or living tissue constructs. To make bioprinting and bioprinted constructs more powerful, machine learning (ML) is introduced to optimize the relevant processes, applied materials, and mechanical/biological performances. The objectives of this work were to collate, analyze, categorize, and summarize published articles and papers pertaining to ML applications in bioprinting and their impact on bioprinted constructs, as well as the directions of potential development. From the available references, both traditional ML and deep learning (DL) have been applied to optimize the printing process, structural parameters, material properties, and biological/ mechanical performance of bioprinted constructs. The former uses features extracted from image or numerical data as inputs in prediction model building, and the latter uses the image directly for segmentation or classification model building. All of these studies present advanced bioprinting with a stable and reliable printing process, desirable fiber/droplet diameter, and precise layer stacking, and also enhance the bioprinted constructs with better design and cell performance. The current challenges and outlooks in developing process–material–performance models are highlighted, which may pave the way for revolutionizing bioprinting technologies and bioprinted construct design.

Keywords
Bioprinting
Machine learning
Deep learning
Biomaterials
Bioprinted constructs
References

1. Gudapati H, Dey M, Ozbolat I, 2016, A comprehensive review on droplet-based bioprinting: Past, present and future. Biomaterials, 102:20–42. https://doi.org/10.1016/j.biomaterials.2016.06.012 

2. Placone JK, Engler AJ, 2018, Recent advances in extrusion‐based 3D printing for biomedical applications. Adv Healthc Mater, 7(8):1701161. https://doi.org/10.1002/adhm.201701161
 
3. Papaioannou TG, Manolesou D, Dimakakos E, et al., 2019, 3D bioprinting methods and techniques: Applications on artificial blood vessel fabrication. Acta Cardiol Sinica, 35(3):284. https://doi.org/10.6515/ACS.201905_35(3).20181115A 

4. He J, Zhang B, Li Z, et al., 2020, High-resolution electrohydrodynamic bioprinting: A new biofabrication strategy for biomimetic micro/nanoscale architectures and living tissue constructs. Biofabrication, 12(4):042002. https://doi.org/10.1088/1758-5090/aba1fa 

5. Ng WL, Chan A, Ong YS, et al., 2020, Deep learning for fabrication and maturation of 3D bioprinted tissues and organs. Virtual Phys Prototyp, 15(3):340–358. https://doi.org/10.1080/17452759.2020.1771741 

6. Regenhu, 2022, The R-GEN 100 bioprinter [EB/OL]. https://www.regenhu.com/3dbioprinting-solutions/r-gen- 100-3dbioprinter (Accessed November 8, 2022) 

7. 3dsman, 2022, EnvisionTEC: 3D-Bioplotter [EB/OL]. https://3dsman.com/envisiontec-3d-bioplotter (Accessed November 8, 2022) 

8. Ozbolat IT, Hospodiuk M, 2016, Current advances and future perspectives in extrusion-based bioprinting. Biomaterials, 76:321–343. https://doi.org/10.1016/j.biomaterials.2015.10.076 

9. Ning L, Chen X, 2017, A brief review of extrusion‐based tissue scaffold bio‐printing. Biotechnol J, 12(8):1600671. https://doi.org/10.1002/biot.201600671 

10. Brown TD, Dalton PD, Hutmacher DW, 2011, Direct writing by way of melt electrospinning. Adv Mater, 23(47):5651– 5657. https://doi.org/10.1002/adma.201103482 

11. Wu Y, Fuh J, Wong Y, et al., 2015, Fabrication of 3D scaffolds via E-jet printing for tendon tissue repair, in International Manufacturing Science and Engineering Conference, 56833, V002T03A005. 

12. Zhang B, Seong B, Nguyen V, et al., 2016, 3D printing of high-resolution PLA-based structures by hybrid electrohydrodynamic and fused deposition modeling techniques. J Micromech Microeng, 26(2):025015. https://doi.org/10.1088/0960-1317/26/2/025015 

13. He J, Xu F, Cao Y, et al., 2016, Towards microscale electrohydrodynamic three-dimensional printing. J Phys D Appl Phys, 49(5):055504. https://doi.org/10.1088/0022-3727/49/5/055504

14. Jing L, Sun J, Liu H, et al., 2021, Using plant proteins to develop composite scaffolds for cell culture applications. Int J Bioprint, 7(1):66–77. https://doi.org/10.18063/ijb.v7i1.298 

15. Sun J, Jing L, Fan X, et al., 2019, Electrohydrodynamic printing process monitoring by microscopic image identification. Int J Bioprint, 5(1):1–9. https://doi.org/10.18063/ijb.v5i1.164 

16. Sun J, Jing L, Liu H, et al., 2020, Generating nanotopography on PCL microfiber surface for better cell-scaffold interactions. Proc Manuf, 48:619–624. https://doi.org/10.1016/j.promfg.2020.05.090 

17. An J, Chua CK, Mironov V, 2021, Application of machine learning in 3D bioprinting: Focus on development of big data and digital twin. Int J Bioprint, 7(1):1–6. https://doi.org/10.18063/ijb.v7i1.342 

18. Sun J, Hong G, Rahman M, et al., 2005, Improved performance evaluation of tool condition identification by manufacturing loss consideration. Int J Prod Res, 43(6):1185–1204. https://doi.org/10.1080/00207540412331299701 

19. Sun J, Yao K, Huang K, et al., 2022, Machine learning applications in scaffold based bioprinting. Mater Today Proc, 70 (2022):17–23. 

20. Jie S, Hong GS, Rahman M, et al., 2002, Feature extraction and selection in tool condition monitoring system, in Australian Joint Conference on Artificial Intelligence, 487–497. 

21. MathWorks, 2022, Support vector machine classification [EB/OL]. https://www.mathworks.com/help/stats/support-vector-machine-classification.html (Accessed November 8, 2022) 

22. Yu C, Jiang J, 2020, A perspective on using machine learning in 3D bioprinting. Int J Bioprint, 6(1):4–11. https://doi.org/10.18063/ijb.v6i1.253 

23. Shin J, Lee Y, Li Z, et al., 2022, Optimized 3D bioprinting technology based on machine learning: A review of recent trends and advances. Micromachines, 13(3):363. https://doi.org/10.3390/mi13030363 

24. Kalantary S, Jahani A, Jahani R, 2020, MLR and Ann approaches for prediction of synthetic/natural nanofibers diameter in the environmental and medical applications. Sci Rep, 10(1):1–10. https://doi.org/10.1038/s41598-020-65121-x 

25. Jin Z, Zhang Z, Shao X, et al., 2021, Monitoring anomalies in 3D bioprinting with deep neural networks. ACS Biomater Sci Eng. https://doi.org/10.1021/acsbiomaterials.0c01761 

26. Conev A, Litsa EE, Perez MR, et al., 2020, Machine learning-guided three-dimensional printing of tissue engineering scaffolds. Tissue Eng Part A, 26(23-24):1359–1368. https://doi.org/10.1089/ten.tea.2020.0191 

27. Fu Z, Angeline V, Sun W, 2021, Evaluation of printing parameters on 3D extrusion printing of pluronic hydrogels and machine learning guided parameter recommendation. Int J Bioprint, 7(4):179–189. https://doi.org/10.18063/ijb.v7i4.434 

28. Ball AK, Das R, Roy SS, et al., 2020, Modeling of EHD inkjet printing performance using soft computing-based approaches. Soft Comput, 24(1):571–589. https://doi.org/10.1007/s00500-019-04202-0 

29. Huang J, Segura LJ, Wang T, et al., 2020, Unsupervised learning for the droplet evolution prediction and process dynamics understanding in inkjet printing. Addit Manuf, 35:101197. https://doi.org/10.1016/j.addma.2020.101197 

30. Ruberu K, Senadeera M, Rana S, et al., 2021, Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing. Appl Mater Today, 22:100914. https://doi.org/10.1016/j.apmt.2020.100914 

31. Das R, Ball AK, Roy SS, 2018, Optimization of E-jet based micro-manufacturing process using desirability function analysis, in Industry Interactive Innovations in Science, Engineering and Technology, Springer, 477–484.
 
32. Lee J, Oh SJ, An SH, et al., 2020, Machine learning-based design strategy for 3D printable bioink: Elastic modulus and yield stress determine printability. Biofabrication, 12(3):035018. https://doi.org/10.1088/1758-5090/ab8707 

33. Bone JM, Childs CM, Menon A, et al., 2020, Hierarchical machine learning for high-fidelity 3D printed biopolymers. ACS Biomater Sci Eng, 6(12):7021–7031. https://doi.org/10.1021/acsbiomaterials.0c00755 

34. Tian S, Stevens R, McInnes BT, et al., 2021, Machine assisted experimentation of extrusion-based bioprinting systems. Micromachines, 12(7):780. https://doi.org/10.3390/mi12070780 

35. Tourlomousis F, Jia C, Karydis T, et al., 2019, Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates. Microsyst Nanoeng, 5(1):1–19. https://doi.org/10.1088/1758-5090/ab8707 

36. Yao K, Huang K, Sun J, et al., 2021, Scaffold-A549: A benchmark 3D fluorescence image dataset for unsupervised nuclei segmentation. Cognit Comput, 13(6):1603–1608. https://doi.org/10.1007/s12559-021-09944-4

37. Sujeeun LY, Goonoo N, Ramphul H, et al., 2020, Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms. R Soc Open Sci, 7(12):201293. 

38. Paddock SW, 1999, Confocal laser scanning microscopy. Biotechniques, 27(5):992–1004. https://doi.org/10.2144/99275ov01 

39. Kalantary S, Jahani A, Pourbabaki R, et al., 2019, Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies. RSC Adv, 9(43):24858–24874. https://doi.org/10.1039/C9RA04927D 

40. Kievit FM, Florczyk SJ, Leung MC, et al., 2010, Chitosan– alginate 3D scaffolds as a mimic of the glioma tumor microenvironment. Biomaterials, 31(22):5903–5910. https://doi.org/10.1016/j.biomaterials.2010.03.062 

41. Lee F, Kurisawa M, 2013, Formation and stability of interpenetrating polymer network hydrogels consisting of fibrin and hyaluronic acid for tissue engineering. Acta Biomater, 9(2):5143–5152. https://doi.org/10.1016/j.actbio.2012.08.036 

42. Fitzgerald KA, Guo J, Tierney EG, et al., 2015, The use of collagen-based scaffolds to simulate prostate cancer bone metastases with potential for evaluating delivery of nanoparticulate gene therapeutics. Biomaterials, 66:53–66. https://doi.org/10.1016/j.biomaterials.2015.07.019 

43. Song Y, Hua S, Sayyar S, et al., 2022, Corneal bioprinting using a high concentration pure collagen I transparent bioink. Bioprinting, 28:e00235. https://doi.org/10.1016/j.bprint.2022.e00235 

44. Reina-Romo E, Mandal S, Amorim P, et al., 2021, Towards the experimentally-informed in silico nozzle design optimization for extrusion-based bioprinting of shear-thinning hydrogels. Front Bioeng Biotechnol, 9:701778. 

45. Xu H, Liu Q, Casillas J, et al., 2020, Prediction of cell viability in dynamic optical projection stereolithography-based bioprinting using machine learning. J Intel Manuf, 33:1–11. 

46. Malekpour A, Chen X, 2022, Printability and cell viability in extrusion-based bioprinting from experimental, computational, and machine learning views. J Funct Biomater, 13(2):40. 

47. Tröndle K, Miotto G, Rizzo L, et al., 2022, Deep learning-assisted nephrotoxicity testing with bioprinted renal spheroids. Int J Bioprint, 8(2):164–173. 

48. Al-Kofahi Y, Zaltsman A, Graves R, et al., 2018, A deep learning-based algorithm for 2-D cell segmentation in microscopy images. BMC Bioinf, 19(1):1–11. 

49. McQuin C, Goodman A, Chernyshev V, et al., 2018, CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol, 16(7):e2005970.
 
50. Shohan S, Harm J, Hasan M, et al., 2021, Non-destructive quality monitoring of 3D printed tissue scaffolds via dielectric impedance spectroscopy and supervised machine learning. Proc Manuf, 53:636–643. 

51. Chen D, Sarkar S, Candia J, et al., 2016, Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues. Biomaterials, 104:104–118. 

52. Yao K, Sun J, Huang K, et al., 2021, Analyzing cell-scaffold interaction through unsupervised 3D nuclei segmentation. Int J Bioprint, 8(1):167–181.
 
53. Yao K, Rochman ND, Sun SX, 2019, Cell type classification and unsupervised morphological phenotyping from low-resolution images using deep learning. Sci Rep, 9(1):1–13. 

54. Sun J, Hong GS, Rahman M, et al., 2004, The application of nonstandard support vector machine in tool condition monitoring system, in Proceedings. DELTA 2004. Second IEEE International Workshop on Electronic Design, Test and Applications, 295–300.

Share
Back to top
International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing