AI-Designed Bioscaffolds
November 24, 2020 | Terry Sharrer
3D printing of bioscaffolds for tissue regeneration involves enough variables that machine learning can sort through the “random forest” of decision trees to reach the optimum choice. That’s what researchers at Rice University did with their poly (propylene fumarate) ink. “The study identified print speed as the most important of five metrics the team measured, the others in descending order of importance being material composition, pressure, layering and spacing.” Besides these technical details, this piece has an interesting aside: i.e. “From start to finish, the COVID-19 window let them assemble data, develop models and get the results published within seven months, record time for a process that can often take years.” MORE
Image Credit: Rice University Department of Computer Science