期刊
BIOENGINEERING-BASEL
卷 9, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/bioengineering9100561
关键词
machine learning; biomaterials; Design of Experiment; tissue engineering; 3d printing
资金
- Science Foundation Ireland (SFI) Centre for Research Training in Artificial Intelligence [18/CRT/6223]
- European Union [814410]
- Science Foundation Ireland (SFI) [SFI/12/RC/2289_P2]
- European Regional Development Fund
Design of Experiments (DoE) methods are commonly used for quantitative data analysis and optimisation, while machine learning (ML) provides greater flexibility in data analysis for various types of data. Research has explored the application of ML in the fields of biomaterials and tissue engineering, particularly in optimizing 3D bioprinting processes.
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.
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