| Autor: |
Al-Kharusi G; School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin 9, Ireland.; Centre for Medical Engineering Research (MEDeng), Dublin City University, Dublin 9, Ireland., Dunne NJ; School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin 9, Ireland.; Centre for Medical Engineering Research (MEDeng), Dublin City University, Dublin 9, Ireland.; Advanced Processing Technology Research Centre, Dublin City University, Dublin 9, Ireland.; Advanced Manufacturing Research Centre (I-Form), Dublin City University, Dublin 9, Ireland.; Biodesign Europe, Dublin City University, Dublin 9, Ireland.; Trinity Centre for Biomedical Engineering (TCBE), Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland.; Advanced Materials and Bioengineering Research Centre (AMBER), Royal College of Surgeons in Ireland and Trinity College Dublin, Dublin 2, Ireland.; School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK., Little S; Insight SFI Research Centre for Data Analytics, Dublin City University, Dublin 9, Ireland., Levingstone TJ; School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin 9, Ireland.; Centre for Medical Engineering Research (MEDeng), Dublin City University, Dublin 9, Ireland.; Advanced Processing Technology Research Centre, Dublin City University, Dublin 9, Ireland.; Advanced Manufacturing Research Centre (I-Form), Dublin City University, Dublin 9, Ireland.; Biodesign Europe, Dublin City University, Dublin 9, Ireland.; Trinity Centre for Biomedical Engineering (TCBE), Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland. |
| Abstrakt: |
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. |
