Artificial intelligence and machine learning could help heal injuries by boosting the development speed of 3D printed bioscaffolds. Bioscaffolds are materials that allow organic objects, like skin and organs, to grow on them. Recent work done by researchers at Rice University applied AI algorithms to the development of bioscaffold materials, with the goal of predicting the quality of printed materials. The researchers found that controlling the speed of the printing is crucial to the development of useful bioscaffold implants.
As reported by ScienceDaily, team of researchers from Rice University collaborated to use machine learning to identify possible improvements to bioscaffold materials. Computer scientist Lydia Kavraki, from the Brown School of Engineering at Rice, lead a research team that applied machine learning algorithms to predict scaffold material quality. The study was co-authored by Rice bioengineer Antonios Mikos, who works on bone-like bioscaffolds that serve as tissue replacements, intended to support the growth of blood vessels and cells and enable wounded tissue to heal more quickly. The bioscaffolds Mikos works on are intended to heal musculoskeletal and craniofacial wounds. The bioscaffolds are produced with the assistance of 3D printing techniques that produce scaffolds that fit the perimeter of a given wound.
The process of 3D printing bioscaffold material requires a lot of trial and error to get the printed batch just right. Various parameters like material composition, structure, and spacing must be taken into account. The application of machine learning techniques can reduce much of this trial and error, giving the engineers useful guidelines that reduce the need to fiddle around with parameters. Kavraki and other researchers were able to give the bioengineering team feedback on which parameters were most important, those most likely to impact the quality of the printed material.
The research team started by analyzing data on printing scaffolds from a 2016 study on biodegradable polypropylene fumarate. Beyond this data, the researchers came up with a set of variables that would help them design a machine learning classifier. Once all the necessary data was collected, the researchers were able to design models, test them, and get the results published in just over half a year.
In terms of the machine learning models used by the research team, the team experimented with two different approaches. Both machine learning approaches were based on random forest algorithms, which aggregate decision trees to achieve a more robust and accurate model. One of the models that the team tested was a binary classification method that predicted if a particular set of parameters would result in a low or high-quality product. Meanwhile, the second classification method utilized a regression-method that estimated which parameter values would give a high-quality result.
According to the results of the research, the most important parameters for high-quality bioscaffolds were spacing, layering, pressure, material composition, and print speed. Print speed was the most important variable overall, followed by material composition. Its hoped that the results of the study will lead to better, faster printing of bioscaffolds, thereby enhancing the reliability of 3D printing body parts like cartilage, kneecaps, and jawbones.
According to Kavraki, the methods used by the research team have the potential to be used at other labs. As Kavraki was quoted by ScienceDaily:
“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.”
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