A research team, led by Lydia Kavraki of Rice University’s Brown School of Engineering, USA, have used Artificial Intelligence (AI) and machine learning technologies to predict the quality of bioscaffolds, a material used to form tissue for missing or damaged organs.
The team discovered that controlling the 3D printing speed can be critical to the quality of bioscaffolds and implants. “We were able to give feedback on which parameters are most likely to affect the quality of printing, so when they continue their experimentation, they can focus on some parameters and ignore the others,” said Kavraki.
A new bone-like scaffold, developed at Rice University’s Brown School of Engineering, has a porous surface which can successfully support the growth of cells and blood vessels and promote the growth of new tissue. In order to improve upon this bioscaffold, Kavraki studied five metrics of the printing process: material composition, pressure, layering and spacing.
In a paper named ‘Machine Learning Guided 3D Printing of Tissue Engineering Scaffolds’, published in Tissue Engineering, the team discussed their two modelling approaches. Firstly, they developed a classification method to predict whether a given set of parameters would produce a low or high quality scaffold. The team then created a regression-based approach that approximated the values of print-quality metrics to come to a result. Kavraki said both relied upon a classical supervised learning technique, called random forest, that builds multiple ‘decision trees’ and merges them to get a more accurate and stable prediction.
Antonios Mikos, a bioengineer at Rice University’s Brown School of Engineering, said: ” This line of research gives us not only the ability to optimise a system for which we have a number of variables – which is very important – but also the possibility to discover something totally new and unexpected. In my opinion, that’s the real beauty of this work.”