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Engineering researcher Aaron Babier has developed automation software that reduce time of cancer treatment – cut the time down to hours. 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, it did so within 20 minutes. 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.
AI may give treatment planners a brilliant head start in helping patients, it doesn’t make the trained human mind obsolete. Once the software has created a treatment plan, it would still be reviewed and further customized by a radiation physicist, which could take up to a few hours.