Page 56 - IJB-9-4

International Journal of Bioprinting

REVIEW ARTICLE
Machine learning and 3D bioprinting

Jie Sun *, Kai Yao , Jia An , Linzhi Jing , Kaizhu Huang *, Dejian Huang 7
1,2
1
3,4
6
5
1 1School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China
2 School of Engineering, University of Liverpool, Liverpool, UK
3 Singapore Centre for 3D Printing, Nanyang Technological University, Singapore
4 Centre for Healthcare Education, Entrepreneurship and Research at SUTD, Singapore University
of Technology and Design, Singapore
5 National University of Singapore Suzhou Research Institute, Suzhou, China
6 Data Science Research Centre, Duke Kunshan University, Kunshan, China
7 National University of Singapore, Singapore
(This article belongs to the Special Issue: Related to 3D printing technology and materials)

Abstract

With 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
*Corresponding authors: from image or numerical data as inputs in prediction model building, and the latter
Jie Sun (Jie.Sun@xjtlu.edu.cn) uses the image directly for segmentation or classification model building. All of these
Kaizhu Huang studies present advanced bioprinting with a stable and reliable printing process,
(kaizhu.huang@dukekunshan.edu.cn)
desirable fiber/droplet diameter, and precise layer stacking, and also enhance
Citation: Sun J, Yao K, An J, et al., the bioprinted constructs with better design and cell performance. The current
2023, Machine learning and 3D
bioprinting. Int J Bioprint, 9(4): 717. challenges and outlooks in developing process–material–performance models are
https://doi.org/10.18063/ijb.717 highlighted, which may pave the way for revolutionizing bioprinting technologies
and bioprinted construct design.
Received: November 28, 2022
Accepted: December 28, 2022
Published Online: March 24, 2023
Keywords: Bioprinting; Machine learning; Deep learning; Biomaterials; Bioprinted
Copyright: © 2023 Author(s). constructs
This is an Open Access article
distributed under the terms of the
Creative Commons Attribution
License, permitting distribution
and reproduction in any medium, 1. Introduction
provided the original work is
properly cited. Three-dimensional (3D) bioprinting can precisely manipulate biomaterials or bioinks and
Publisher’s Note: Whioce fabricate constructs with well-defined microstructures in a controllable and reproducible
Publishing remains neutral with manner. Such constructs can provide 3D environments for in vitro studies in cell biology,
regard to jurisdictional claims in tissue engineering, and drug screening [1-3] . A growing number of biomaterials and printing
published maps and institutional [3,4]
affiliations. technologies are available to fabricate such constructs . This creates a tremendous

Volume 9 Issue 4 (2023) 48 https://doi.org/10.18063/ijb.717