Page 341 - IJB-9-4

International Journal of Bioprinting

REVIEW ARTICLE
Machine learning boosts three-dimensional

bioprinting

Hongwei Ning , Teng Zhou *, Sang Woo Joo *
1
3
2
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
(This article belongs to the Special Issue: Advances in 3D bioprinting for regenerative medicine and
drug screening)

Abstract

Three-dimensional (3D) bioprinting is a computer-controlled technology that
combines biological factors and bioinks to print an accurate 3D structure in a layer-
by-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. Data-
driven 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
*Corresponding authors:
Sang Woo Joo paper introduces several machine learning algorithms in detail, summarizes the
(swjoo@yu.ac.kr) role of machine learning in additive manufacturing applications, and reviews the
Teng Zhou research progress of the combination of 3D bioprinting and machine learning in
(zhouteng@hainanu.edu.cn)
recent years, especially the improvement of bioink generation, the optimization of
Citation: Ning H, Zhou T, Joo SW, printing parameter, and the detection of printing defect.
2023, Machine learning boosts
three-dimensional bioprinting.
Int J Bioprint, 9(4): 739. Keywords: Bioprinting; Additive manufacturing; K-nearest neighbor;
https://doi.org/10.18063/ijb.739
Long short-term memory; Ensemble learning
Received: February 03, 2023
Accepted: March 06, 2023
Published Online: April 27, 2023
Copyright: © 2023 Author(s). 1. Introduction
This is an Open Access article
distributed under the terms of the Three-dimensional (3D) printing technology, which is also called additive
Creative Commons Attribution
License, permitting distribution, manufacturing, is a branch of rapid prototyping technology. It is a manufacturing
and reproduction in any medium, technology that accumulates materials layer by layer and solidifies them to obtain solid
provided the original work is finished products [1,2] . The 3D model obtained by computer rendering or scanning is first
properly cited.
discretized into a stack of parallel layers by slicing software. Then, through the numerical
Publisher’s Note: Whioce control system, spraying, extrusion, hot melting, laser, and other methods, the filament-
Publishing remains neutral with
regard to jurisdictional claims in like, liquid or powdered plastic, ceramic, metal, and other materials are positioned,
[3]
published maps and institutional scanned, and stacked layer by layer. Finally, the printed solid product is obtained .
affiliations. In recent years, 3D printing technology has attracted much attention because of its

Volume 9 Issue 4 (2023) 333 https://doi.org/10.18063/ijb.739