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A Perspective on Using Machine Learning in 3D Bioprinting

Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printin...

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Detalles Bibliográficos
Autores principales: Yu, Chunling, Jiang, Jingchao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Whioce Publishing Pte. Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7415853/
https://www.ncbi.nlm.nih.gov/pubmed/32782987
http://dx.doi.org/10.18063/ijb.v6i1.253
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author Yu, Chunling
Jiang, Jingchao
author_facet Yu, Chunling
Jiang, Jingchao
author_sort Yu, Chunling
collection PubMed
description Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printing processes, such as process optimization, dimensional accuracy analysis, manufacturing defect detection, and material property prediction. However, few studies have been found to use machine learning in 3D bioprinting processes. In this paper, related machine learning methods used in 3D printing are briefly reviewed and a perspective on how machine learning can also benefit 3D bioprinting is discussed. We believe that machine learning can significantly affect the future development of 3D bioprinting and hope this paper can inspire some ideas on how machine learning can be used to improve 3D bioprinting.
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spelling pubmed-74158532020-08-10 A Perspective on Using Machine Learning in 3D Bioprinting Yu, Chunling Jiang, Jingchao Int J Bioprint Perspective Article Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printing processes, such as process optimization, dimensional accuracy analysis, manufacturing defect detection, and material property prediction. However, few studies have been found to use machine learning in 3D bioprinting processes. In this paper, related machine learning methods used in 3D printing are briefly reviewed and a perspective on how machine learning can also benefit 3D bioprinting is discussed. We believe that machine learning can significantly affect the future development of 3D bioprinting and hope this paper can inspire some ideas on how machine learning can be used to improve 3D bioprinting. Whioce Publishing Pte. Ltd. 2020-01-24 /pmc/articles/PMC7415853/ /pubmed/32782987 http://dx.doi.org/10.18063/ijb.v6i1.253 Text en Copyright: © 2020 Yu and Jiang. http://creativecommons.org/licenses/cc-by-nc/4.0/ This is an open-access article distributed under the terms of the Attribution-NonCommercial 4.0 International 4.0 (CC BY-NC 4.0), which permits all non-commercial use, distribution, and reproduction in any medium provided the original work is properly cited.
spellingShingle Perspective Article
Yu, Chunling
Jiang, Jingchao
A Perspective on Using Machine Learning in 3D Bioprinting
title A Perspective on Using Machine Learning in 3D Bioprinting
title_full A Perspective on Using Machine Learning in 3D Bioprinting
title_fullStr A Perspective on Using Machine Learning in 3D Bioprinting
title_full_unstemmed A Perspective on Using Machine Learning in 3D Bioprinting
title_short A Perspective on Using Machine Learning in 3D Bioprinting
title_sort perspective on using machine learning in 3d bioprinting
topic Perspective Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7415853/
https://www.ncbi.nlm.nih.gov/pubmed/32782987
http://dx.doi.org/10.18063/ijb.v6i1.253
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