AccScience Publishing / IJB / Volume 9 / Issue 4 / DOI: 10.18063/ijb.739
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Machine learning boosts three-dimensional bioprinting

Hongwei Ning1 Teng Zhou2* Sang Woo Joo3*
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1 College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, Anhui, China
2 Mechanical and Electrical Engineering College, Hainan University, Haikou, Hainan, China
3 School of Mechanical Engineering, Yeungnam University, Gyeongsan, Korea
Submitted: 3 February 2023 | Accepted: 6 March 2023 | Published: 27 April 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

Correction for this article is available: view correction


Three-dimensional (3D) bioprinting is a computer-controlled technology that combines biological factors and bioinks to print an accurate 3D structure in a layerby-layer fashion. 3D bioprinting is a new tissue engineering technology based on rapid prototyping and additive manufacturing technology, combined with various disciplines. In addition to the problems in in vitro culture process, the bioprinting procedure is also afflicted with a few issues: (1) difficulty in looking for the appropriate bioink to match the printing parameters to reduce cell damage and mortality; and (2) difficulty in improving the printing accuracy in the printing process. Datadriven machine learning algorithms with powerful predictive capabilities have natural advantages in behavior prediction and new model exploration. Combining machine learning algorithms with 3D bioprinting helps to find more efficient bioinks, determine printing parameters, and detect defects in the printing process. This paper introduces several machine learning algorithms in detail, summarizes the role of machine learning in additive manufacturing applications, and reviews the research progress of the combination of 3D bioprinting and machine learning in recent years, especially the improvement of bioink generation, the optimization of printing parameter, and the detection of printing defect.

Additive manufacturing
K-nearest neighbor
Long short-term memory
Ensemble learning

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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing