Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169861
Title: Machine learning and 3D bioprinting
Authors: Sun, Jie
Yao, Kai
An, Jia
Jing, Linzhi
Huang, Kaizhu
Huang, Dejian
Keywords: Engineering::Mechanical engineering
Issue Date: 2023
Source: Sun, J., Yao, K., An, J., Jing, L., Huang, K. & Huang, D. (2023). Machine learning and 3D bioprinting. International Journal of Bioprinting, 9(4), 48-61. https://dx.doi.org/10.18063/ijb.717
Journal: International Journal of Bioprinting 
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.
URI: https://hdl.handle.net/10356/169861
ISSN: 2424-7723
DOI: 10.18063/ijb.717
Schools: School of Mechanical and Aerospace Engineering 
Organisations: Centre for Healthcare Education, Entrepreneurship and Research at SUTD 
Research Centres: Singapore Centre for 3D Printing 
Rights: © 2023 Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License, permitting distribution and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SC3DP Journal Articles

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