According to researchers at Rice University, a dash of artificial intelligence can speed the making of 3D-printed bioscaffolds that assist injuries to heal.
A team of researchers at Brown School of Engineering, Rice University employed a machine learning approach. Given printing parameters, the team employed this approach to predict the quality of scaffold materials. The work also found that controlling print speed is crucial in making high-quality implants.
In appearance, the bioscaffolds developed are bonelike that serve as placeholders for injured tissue. The porosity in these bioscaffolds supports the growth of cells and blood vessels that convert into new tissue and finally replace the implant.
Research points immense scope for improvement of Bioscaffolds
Meanwhile, in association with the Center for Engineering Complex Tissues, the lead researcher has been developing bioscaffolds. This is to improve techniques to heal musculoskeletal and craniofacial wounds.
This, however, does not imply there is no scope of improvement. Using machine learning techniques, the design of materials and developing processes to fabricate implants can be faster and eliminates chance of trial and error.
The feedback on the parameters that affect the quality of printing given. Therefore, when experimentation continues, the focus can be on certain parameters and others can be ignored.
Meanwhile, the study recognized print speed as the most important metrics measured by the team. Material composition, pressure, layering and spacing are other metrics in the descending order of importance.
Earlier, the team of researchers considered bringing machine learning into the mix. And, the COVID-19 pandemic provided a unique opportunity to carry out the project. The approach served to be a great way for progress. Nonetheless, many faculty members and students were unable to reach the lab.