The work also found that controlling print speed is critical in making high-quality implants. Bioscaffolds developed by co-author and Rice bioengineer Antonios Mikos are bonelike structures that serve as placeholders for injured tissue. They are porous to support the growth of cells and blood vessels that turn into new tissue and ultimately replace the implant.
Mikos has been developing bioscaffolds, largely in concert with the Center for Engineering Complex Tissues, to improve techniques to heal craniofacial and musculoskeletal wounds. That work has progressed to include sophisticated 3D printing that can make a biocompatible implant custom-fit to the site of a wound.
With the help of machine learning techniques, designing materials and developing processes to create implants can be faster and eliminate much trial and error. "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 renowned authority on robotics, artificial intelligence and biomedicine and director of Rice's Ken Kennedy Institute.
Kavraki said the researchers - graduate students Anja Conev and Eleni Litsa in her lab and graduate student Marissa Perez and postdoctoral fellow Mani Diba in the Mikos lab, all co-authors of the paper - took time at the start to establish an approach to a mass of data from a 2016 study on printing scaffolds with biodegradable poly(propylene fumarate), and then to figure out what more was needed to train the computer models.
The team explored two modeling approaches. One was a classification method that predicted whether a given set of parameters would produce a "low" or "high" quality scaffold. The other was 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 together to get a more accurate and stable prediction. Ultimately, the collaboration could lead to better ways to quickly print a customized jawbone, kneecap or bit of cartilage on demand.
"A hugely important aspect is the potential to discover new things," Mikos said. "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. "It's a great example of convergence," he said. "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."
"In the long run, labs should be able to understand which of their materials can give them different kinds of printed scaffolds, and in the very long run, even predict results for materials they have not tried. We don't have enough data to do that right now, but at some point we think we should be able to generate such models," Kavraki said. "Artificial intelligence has a role to play in new materials, so what the institute offers should be of interest to people on this campus," she said. "There are so many problems at the intersection of materials science and computing, and the more people we can get to work on them, the better."
The research was published Tissue Engineering Part A.