Machine learning and 3D bioprinting
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 re...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/169861 |
| _version_ | 1811691645365649408 |
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| author | Sun, Jie Yao, Kai An, Jia Jing, Linzhi Huang, Kaizhu Huang, Dejian |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Sun, Jie Yao, Kai An, Jia Jing, Linzhi Huang, Kaizhu Huang, Dejian |
| author_sort | Sun, Jie |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:23:11Z |
| format | Journal Article |
| id | ntu-10356/169861 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:23:11Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1698612023-08-09T15:37:18Z Machine learning and 3D bioprinting Sun, Jie Yao, Kai An, Jia Jing, Linzhi Huang, Kaizhu Huang, Dejian School of Mechanical and Aerospace Engineering Centre for Healthcare Education, Entrepreneurship and Research at SUTD Singapore Centre for 3D Printing Engineering::Mechanical engineering Bioprinting Machine Learning 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. Published version This work was financially supported by Xi’an Jiaotong-Liverpool University’s Key Program Special Fund under Grant KSF-E-37. 2023-08-08T05:22:39Z 2023-08-08T05:22:39Z 2023 Journal Article 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 2424-7723 https://hdl.handle.net/10356/169861 10.18063/ijb.717 37323491 2-s2.0-85160452242 4 9 48 61 en International Journal of Bioprinting © 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. application/pdf |
| spellingShingle | Engineering::Mechanical engineering Bioprinting Machine Learning Sun, Jie Yao, Kai An, Jia Jing, Linzhi Huang, Kaizhu Huang, Dejian Machine learning and 3D bioprinting |
| title | Machine learning and 3D bioprinting |
| title_full | Machine learning and 3D bioprinting |
| title_fullStr | Machine learning and 3D bioprinting |
| title_full_unstemmed | Machine learning and 3D bioprinting |
| title_short | Machine learning and 3D bioprinting |
| title_sort | machine learning and 3d bioprinting |
| topic | Engineering::Mechanical engineering Bioprinting Machine Learning |
| url | https://hdl.handle.net/10356/169861 |
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