Think “scaffolds” and you’re likely to call to mind skeletal-looking structures set up alongside buildings with laborers clambering about attending to construction or repair.
But according to researchers at Rice University, machine learning techniques have been enlisted that have been able to predict the quality of so-called “bioscaffold materials” used to help tissue injuries heal.
As an article at HealthITAnalytics explains it, “bioscaffolds are bonelike structures that serve as placeholders for injured tissue. These structures are porous to support the growth of cells and blood vessels that turn into new tissue and ultimately replace the implant.”
The Rice U. research team has been developing bioscaffolds to improve techniques to heal craniofacial and musculoskeletal wounds, and the “work has progressed to include sophisticated 3D printing that can make a biocompatible implant custom-fit to the site of a wound.”
The researchers found that print speed was the most important of the five metrics measured, followed in descending order of importance by material composition, pressure, layering, and spacing.
“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 Lydia Kavraki, an authority on robotics, artificial intelligence and biomedicine and director of Rice’s Ken Kennedy Institute.
According to the researchers, they explored two machine learning modeling approaches, one being a classification method that predicted whether a given set of parameters would produce a low- or high-quality scaffold, while the other was a regression-based approach that estimated the values of print-quality metrics to come to a conclusion. Both methods relied on a classic supervised learning technique that builds multiple decision trees and merges them for a more accurate and stable prediction.
The results showed that the approach could speed the development of 3D-printed bioscaffolds that help injuries heal, and the method could ultimately lead to better ways to quickly print a customized jawbone, kneecap, or a bit of cartilage on demand.
“A hugely important aspect is the potential to discover new things. This line of research gives us not only the ability to optimize 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,” said Antonios Mikos, a bioengineer at Rice University.
“It’s a great example of convergence. We have a lot to learn from advances in computer science and artificial intelligence, and this study is a perfect example of how they will help us become more efficient.”