Page 331 - IJB-9-6
P. 331
International Journal of Bioprinting Rheology-informed machine learning model
33. Song K, Zhang D, Yin J, et al., 2021, Computational study of 44. Bone JM, Childs CM, Menon A, et al., 2020, Hierarchical
extrusion bioprinting with jammed gelatin microgel-based machine learning for high-fidelity 3D printed biopolymers.
composite ink. Addit Manuf, 41: 101963. ACS Biomater Sci Eng, 6(12): 7021–7031.
https://doi.org/10.1016/j.addma.2021.101963 https://doi.org/10.1021/acsbiomaterials.0c00755
34. Leppiniemi J, Lahtinen P, Paajanen A, et al., 2017, 45. Menon A, Póczos B, Feinberg AW, et al., 2019, Optimization
3D-printable bioactivated nanocellulose–alginate hydrogels. of silicone 3D printing with hierarchical machine learning.
ACS Appl Mater Interfaces, 9(26): 21959–21970. 3D Print Addit Manuf, 6(4): 181–189.
https://doi.org/10.1021/acsami.7b02756 https://doi.org/10.1089/3dp.2018.0088
35. Kim MH, Lee YW, Jung W-K, et al., 2019, Enhanced 46. Jin Z, Zhang Z, Shao X, et al., 2021, Monitoring anomalies
rheological behaviors of alginate hydrogels with carrageenan in 3D bioprinting with deep neural networks. ACS Biomater
for extrusion-based bioprinting. J Mech Behav Biomed Sci Eng, 9(7): 3945–3952.
Mater, 98: 187–194.
https://doi.org/10.1016/j.jmbbm.2019.06.014 https://doi.org/10.1021/acsbiomaterials.0c01761
36. Göhl J, Markstedt K, Mark A, et al., 2018, Simulations of 3D 47. Mancha Sánchez E, Gómez-Blanco JC, López Nieto, et al.,
bioprinting: Predicting bioprintability of nanofibrillar inks. 2020, Hydrogels for bioprinting: A systematic review of
Biofabrication, 10(3): 034105. hydrogels synthesis, bioprinting parameters, and bioprinted
structures behavior. Front Bioeng Biotechnol, 8: 776.
https://doi.org/10.1088/1758-5090/aac872
https://doi.org/10.3389/fbioe.2020.00776
37. Bonatti AF, Chiesa I, Vozzi G, et al., 2021, Open-source
CAD-CAM simulator of the extrusion-based bioprinting 48. Zhang Z, Jin Y, Yin J, et al., 2018, Evaluation of bioink
process. Bioprinting, 24: e00172. printability for bioprinting applications. Appl Phys Rev, 5(4):
041304.
https://doi.org/10.1016/j.bprint.2021.e00172
38. Suntornnond R, Tan EYS, An J, et al., 2016, A mathematical https://doi.org/10.1063/1.5053979
model on the resolution of extrusion bioprinting for the 49. Chimene D, Lennox KK, Kaunas RR, et al., 2016, Advanced
development of new bioinks. Materials, 9(9): 756. bioinks for 3D printing: A materials science perspective.
https://doi.org/10.3390/ma9090756 Annal Biomed Eng, 44(6): 2090–2102.
39. Bonatti AF, Vozzi G, Chua CK, et al., A deep learning https://doi.org/10.1007/s10439-016-1638-y
approach for error detection and quantification in extrusion- 50. Cooke ME, Rosenzweig DH, 2021, The rheology of direct and
based bioprinting. Mater Today: Proc, 70: 131–135. suspended extrusion bioprinting. APL Bioeng, 5(1): 011502.
https://doi.org/10.1016/j.matpr.2022.09.006 https://doi.org/10.1063/5.0031475
40. Ruberu K, Senadeera M, Rana S, et al., 2021, Coupling 51. Paxton N, Smolan W, Böck T, et al., 2017, Proposal to assess
machine learning with 3D bioprinting to fast track printability of bioinks for extrusion-based bioprinting
optimisation of extrusion printing. Appl Mater Today, 22: and evaluation of rheological properties governing
100914.
bioprintability. Biofabrication, 9(4): 044107.
https://doi.org/10.1016/j.apmt.2020.100914
https://doi.org/10.1088/1758-5090/aa8dd8
41. Lee J, Oh SJ, An SH, et al., 2020, Machine learning-based
design strategy for 3D printable bioink: Elastic modulus 52. Ouyang L, Yao R, Zhao Y, et al., 2016, Effect of bioink
and yield stress determine printability. Biofabrication, 12(3): properties on printability and cell viability for 3D bioplotting
035018. of embryonic stem cells. Biofabrication, 8(3): 035020.
https://doi.org/10.1088/1758-5090/ab8707 http://dx.doi.org/10.1088/1758-5090/8/3/035020
42. Conev A, Litsa EE, Perez MR, et al., 2020, Machine learning- 53. Xu X, Jagota A, Peng S, et al., 2013, Gravity and surface
guided three-dimensional printing of tissue engineering tension effects on the shape change of soft materials.
scaffolds. Tissue Eng Part A, 26(23–24): 1359–1368. Langmuir, 29(27): 8665–8674.
https://doi.org/10.1089/ten.tea.2020.0191 https://doi.org/10.1021/la400921h
43. Fu Z, Angeline V, Sun W, 2021, Evaluation of printing 54. Gao T, Gillispie GJ, Copus JS, et al., 2018, Optimization
parameters on 3D extrusion printing of pluronic hydrogels of gelatin–alginate composite bioink printability
and machine learning guided parameter recommendation. using rheological parameters: A systematic approach.
Int J Bioprint, 7(4): 179–189. Biofabrication, 10(3): 034106.
https://doi.org/10.18063%2Fijb.v7i4.434 https://doi.org/10.1088/1758-5090/aacdc7
Volume 9 Issue 6 (2023) 323 https://doi.org/10.36922/ijb.1280

