Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

The role of machine learning and design of experiments in the advancement of biomaterial and tissue engineering research

Al-Kharusi, Ghayadah, Dunne, Nicholas J. orcid logoORCID: 0000-0003-4649-2410, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471 and Levingstone, Tanya J. orcid logoORCID: 0000-0002-9751-2314 (2022) The role of machine learning and design of experiments in the advancement of biomaterial and tissue engineering research. Bioengineering, 9 (10). ISSN 2306-5354

Abstract
Optimisation of tissue engineering (TE) processes requires models that can identify relationships between the parameters to be optimised and predict structural and performance outcomes from both physical and chemical processes. Currently, Design of Experiments (DoE) methods are commonly used for optimisation purposes in addition to playing an important role in statistical quality control and systematic randomisation for experiment planning. DoE is only used for the analysis and optimisation of quantitative data (i.e., number-based, countable or measurable), while it lacks the suitability for imaging and high dimensional data analysis. Machine learning (ML) offers considerable potential for data analysis, providing a greater flexibility in terms of data that can be used for optimisation and predictions. Its application within the fields of biomaterials and TE has recently been explored. This review presents the different types of DoE methodologies and the appropriate methods that have been used in TE applications. Next, ML algorithms that are widely used for optimisation and predictions are introduced and their advantages and disadvantages are presented. The use of different ML algorithms for TE applications is reviewed, with a particular focus on their use in optimising 3D bioprinting processes for tissue-engineered construct fabrication. Finally, the review discusses the future perspectives and presents the possibility of integrating DoE and ML in one system that would provide opportunities for researchers to achieve greater improvements in the TE field.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:machine learning; biomaterials; Design of Experiment; tissue engineering; 3d printing
Subjects:Engineering > Biomedical engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Mechanical and Manufacturing Engineering
Research Initiatives and Centres > Advanced Processing Technology Research Centre (APTRC)
Research Initiatives and Centres > I-Form
Publisher:MDPI
Official URL:https://doi.org/10.3390/bioengineering9100561
Copyright Information:© 2022 The Authors.
Funders:Science Foundation Ireland (SFI) Centre for Research Training in Artificial Intelligence, Grant number 18/CRT/6223, European Union’s Horizon 2020 research and innovation program under grant agreement No 814410 (GIOTTO), Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2, European Regional Development Fund
ID Code:29564
Deposited On:07 Feb 2024 12:53 by Thomas Murtagh . Last Modified 07 Feb 2024 12:53
Documents

Full text available as:

[thumbnail of bioengineering-09-00561-v2.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Creative Commons: Attribution 4.0
4MB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record