A dose of artificial intelligence can speed the development of 3D-printed bioscaffolds that help injuries heal, according to researchers at Rice University.

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A team led by computer scientist Lydia Kavraki of Rice’s Brown School of Engineering used a machine learning approach to predict the quality of scaffold materials, given the printing parameters. The work also found that controlling print speed is critical in making high-quality implants.

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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 […]

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