‘As a researcher, I've found AMAD to be an invaluable tool for collaborating with others and for data processing in research projects,’ says Juha Koivisto, Research Fellow, Department of Applied Physics. Juha is not the only person who feels that the Aalto Materials Digitalisation Platform (AMAD) opens new possibilities for data sharing and collaboration within and between research groups.
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Abstract
3D bioprinting, a paradigm shift in tissue engineering holds a promising perspective for regenerative medicine and disease modelling. 3D scaffolds are fabricated for subsequent cell seeding or incorporated directly to the bioink to create cell-laden 3D constructs. A plethora of factors relating to bioink properties, printing parameters and post print curing play a significant role in the optimisation of the printing process. Although qualitative evaluation of printability has been investigated largely, there is a paucity of studies on quantitative approaches to assess printability. Hence, this study explores machine learning as a novel tool to evaluate printability quantitatively and to fast track optimisation of extrusion printing in achieving a reproducible 3D scaffold. Bayesian Optimisation, a machine learning method has been employed for optimising 3D bioplotting with a scoring system established to assess the printability of gelatin methacryloyl (GelMA) and hyaluronic