Source: news.engineering.utoronto.ca Author: Brian Tran Aaron Babier (MIE PhD candidate) demonstrates his AI-based software’s visualization capabilities. (Credit: Brian Tran) Beating cancer is a race against time. Developing radiation therapy plans — individualized maps that help doctors determine where to blast tumours — can take days. Now, Aaron Babier (MIE PhD candidate) has developed automation software that aims to cut the time down to mere hours. He, along with co-authors Justin Boutilier (MIE PhD 1T8), supervisor Professor Timothy Chan (MIE) and Professor Andrea McNiven (Faculty of Medicine) are looking at radiation therapy design as an intricate — but solvable — optimization problem. Their software uses artificial intelligence (AI) to mine historical radiation therapy data. This information is then applied to an optimization engine to develop treatment plans. The researchers applied this software tool in their study of 217 patients with throat cancer, who also received treatments developed using conventional methods. The therapies generated by Babier’s AI achieved comparable results to patients’ conventionally planned treatments. — and it did so within 20 minutes. The researchers recently published their findings in Medical Physics. “There have been other AI optimization engines that have been developed. The idea behind ours is that it more closely mimics the current clinical best practice,” says Babier. If AI can relieve clinicians of the optimization challenge of developing treatments, more resources are available to improve patient care and outcomes in other ways. Health-care professionals can divert their energy to increasing patient comfort and easing distress. “Right now [...]