Using machine learning to speed bioscaffold development

A team led by computer scientist Lydia Kavraki used a machine learning approach to predict the quality of scaffold materials produced by 3D-printing, given the printing parameters. The work also found that controlling print speed is critical in making high-quality implants. Bioscaffolds developed by co-author and 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 to improve techniques to heal craniofacial and musculoskeletal wounds.