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: 0000-0003-4649-2410, Little, SuzanneORCID: 0000-0003-3281-3471 and Levingstone, Tanya J.ORCID: 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
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
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