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Machine learning and 3D bioprinting
48With 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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Whioce Publishing Pte. Ltd.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10261141/ https://www.ncbi.nlm.nih.gov/pubmed/37323491 http://dx.doi.org/10.18063/ijb.717 |
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author | Sun, Jie Yao, Kai An, Jia Jing, Linzhi Huang, Kaizhu Huang, Dejian |
author_facet | Sun, Jie Yao, Kai An, Jia Jing, Linzhi Huang, Kaizhu Huang, Dejian |
author_sort | Sun, Jie |
collection | PubMed |
description | 48With 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. |
format | Online Article Text |
id | pubmed-10261141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Whioce Publishing Pte. Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102611412023-06-15 Machine learning and 3D bioprinting Sun, Jie Yao, Kai An, Jia Jing, Linzhi Huang, Kaizhu Huang, Dejian Int J Bioprint Research Article 48With 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. Whioce Publishing Pte. Ltd. 2023-03-24 /pmc/articles/PMC10261141/ /pubmed/37323491 http://dx.doi.org/10.18063/ijb.717 Text en Copyright: © 2023, Sun J, Yao K, An J, et al. https://creativecommons.org/licenses/by-nc/4.0/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. |
spellingShingle | Research Article 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 | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10261141/ https://www.ncbi.nlm.nih.gov/pubmed/37323491 http://dx.doi.org/10.18063/ijb.717 |
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