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International Journal of Bioprinting                                      Bioprinting with machine learning



               deep-learning convolutional neural networks for landslide   82.  Guan J, You S, Xiang Y, et al., 2021, Compensating the cell-
               detection[J]. Remote Sens, 11(2):196.              induced light scattering effect in light-based bioprinting
                                                                  using deep learning. Biofabrication, 14(1):015011.
               https://doi.org/10.3390/rs11020196
                                                                  https://doi.org/10.1088/1758-5090/ac3b92
            72.  Khan A, Sohail A, Zahoora U, et al., 2020, A survey of the
               recent architectures of deep convolutional neural networks.   83.  Bone JM, Childs CM, Menon A, et al., 2020, Hierarchical
               Artif Intell Rev, 53(8):5455–5516.                 machine learning for high-fidelity 3D printed biopolymers.
                                                                  ACS Biomater Sci Eng, 6(12):7021–7031.
               https://doi.org/10.1007/s10462-020-09825-6
                                                                  https://doi.org/10.1021/acsbiomaterials.0c00755
            73.  Reina-Romo E, Mandal S, Amorim P, et al., 2021, Towards
               the experimentally-informed in silico nozzle design   84.  Shi J, Song J, Song B, et al., 2019, Multi-objective optimization
               optimization for extrusion-based bioprinting of shear-  design through machine learning for drop-on-demand
               thinning hydrogels. Front Bioeng Biotechnol, 9:701778.  bioprinting. Engineering, 5(3):586–593.
            74.  Allencherry J, Pradeep N, Shrivastava R, et al., 2022,   https://doi.org/10.1016/j.eng.2018.12.009
               Investigation of hydrogel and gelatin bath formulations   85.  Shi J, Wu B, Song B,  et  al., 2018, Learning-based cell
               for extrusion-based 3D bioprinting using deep learning.   injection control for precise drop-on-demand cell printing.
               Procedia CIRP, 110:360–365.                        Ann Biomed Eng, 46(9):1267–1279.

               https://doi.org/10.1016/j.procir.2022.06.064       https://doi.org/10.1007/s10439-018-2054-2
            75.  Xu H, Liu Q, Casillas J, et al., 2022, Prediction of cell   86.  Fu Z, Angeline V, Sun W, 2021, Evaluation of printing
               viability in dynamic optical projection stereolithography-  parameters on 3D extrusion printing of pluronic hydrogels
               based bioprinting using machine learning.  J Intell Manuf,   and machine learning guided parameter recommendation.
               33(4):995–1005.                                    Int J Bioprint, 7(4):434.
               https://doi.org/10.1007/s10845-020-01708-5      87.  Tian S, Stevens R, McInnes BT, et al., 2021, Machine assisted
                                                                  experimentation of extrusion-based bioprinting systems.
            76.  Wu D, Xu C, 2018, Predictive modeling of droplet formation   Micromachines, 12(7):780.
               processes in inkjet-based bioprinting. J Manuf Sci E-T Asme,
               140(10):101007.                                 88.  Ruberu K, Senadeera M, Rana S, et al., 2021, Coupling machine
                                                                  learning with 3D bioprinting to fast track optimisation of
               https://doi.org/10.1115/1.4040619                  extrusion printing. Appl Mater Today, 22:100914.
            77.  Lee J, Oh SJ, An SH, et al., 2020, Machine learning-based   https://doi.org/10.1016/j.apmt.2020.100914
               design strategy for 3D printable bioink: Elastic modulus
               and yield stress determine printability.  Biofabrication,   89.  Jin Z, Zhang Z, Shao X, et al., 2021, Monitoring anomalies
               12(3):035018.                                      in 3D bioprinting with deep neural networks. ACS Biomater
                                                                  Sci Eng.
               https://doi.org/10.1088/1758-5090/ab8707
                                                                  https://doi.org/10.1021/acsbiomaterials.0c01761
            78.  Yamanishi C,  Parigoris E,  Takayama S, 2020,  Kinetic
               analysis of label-free microscale collagen gel contraction   90.  Bonatti AF, Vozzi G, Kai Chua C,  et  al., 2022, A deep
               using machine learning-aided image analysis. Front Bioeng   learning approach for error detection and quantification in
               Biotechnol, 8:582602.                              extrusion-based bioprinting. Mater Today, 70:131–135.
                                                                  https://doi.org/10.1016/j.matpr.2022.09.006
            79.  Tourlomousis  F,  Jia  C,  Karydis  T, et al.,  2019,  Machine
               learning metrology of cell confinement in melt electrowritten   91.  Tebon  PJ, Wang  B,  Markowitz  AL, et al.,  2022, Drug
               three-dimensional  biomaterial  substrates.  Microsyst   screening at single-organoid resolution via bioprinting and
               Nanoeng, 5(1):1–19.                                interferometry. bioRxiv, 2021:2021–2031.
            80.  Safir F, Vu N, Tadesse LF, et al., 2022, Detecting bacteria   92.  Tröndle K, Miotto G, Rizzo L, et al., 2022, Deep learning-
               in multi-cellular samples with combined acoustic   assisted nephrotoxicity testing with bioprinted renal
               bioprinting and Raman spectroscopy.  arXiv  preprint   spheroids. Int J Bioprint, 8(2):528.
               arXiv:2206.09304.
            81.  Nadernezhad A, Groll J, 2022, Machine learning reveals a
               general understanding of printability in formulations based
               on rheology additives. Adv Sci, 9(29):2202638.
               https://doi.org/10.1002/advs.202202638





            Volume 9 Issue 4 (2023)                        350                         https://doi.org/10.18063/ijb.739
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