Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/154957
Title: | Application of machine learning in 3D bioprinting: focus on development of big data and digital twin | Authors: | An, Jia Chua, Chee Kai Mironov, Vladimir |
Keywords: | Engineering::Mechanical engineering | Issue Date: | 2021 | Source: | An, J., Chua, C. K. & Mironov, V. (2021). Application of machine learning in 3D bioprinting: focus on development of big data and digital twin. International Journal of Bioprinting, 7(1), 342-. https://dx.doi.org/10.18063/ijb.v7i1.342 | Journal: | International Journal of Bioprinting | Abstract: | The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future. | URI: | https://hdl.handle.net/10356/154957 | ISSN: | 2424-8002 | DOI: | 10.18063/ijb.v7i1.342 | Schools: | School of Mechanical and Aerospace Engineering | Research Centres: | Singapore Centre for 3D Printing | Rights: | © 2021 An, et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | MAE Journal Articles SC3DP Journal Articles |
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342-1485-1-PB.pdf | 1.04 MB | Adobe PDF | View/Open |
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