IMAGE: U of T Engineering researcher Aaron Babier demonstrates his AI-based software’s visualization capabilities. view more
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, engineering researcher Aaron Babier has developed automation software that aims to cut the time down to mere hours.
He and his team at the University of Toronto’s Department of Mechanical & Industrial Engineering, including Justin Boutilier, supervisor Professor Timothy Chan and Professor Andrea McNiven of U of T’s 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
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