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How lasers and AI could help detect cancerous tissue in minutes


A cancer detecting system built from scratch at the University of Waterloo (UW) is nearing completion and could be on the market within two years.

An engineering team began work on the Photon Absorption Remote Sensing (PARS) system in 2017. It aims to speed up the process of detecting cancerous tissue in patients which, in turn, could speed up treatment.

“With one look, we can get all the answers,” Parsin Haji Reza, a professor and the project’s lead researcher at UW said.

It uses lasers to read a tissue sample, generate data and then feed that data to an AI system.

“The AI basically translates this data – this multi-dimensional data – into information and into images that doctors, pathologists and clinicians can read,” Reza said, adding it should take less than half an hour in total.

He said the traditional biopsy process involves taking a piece of tissue, dividing it, dyeing the sections and then analyzing them under a microscope to look for cancer markers.

An engineering team at UW began work on the Photon Absorption Remote Sensing (PARS) system in 2017. (CTV News/Stefanie Davis)

It sometimes takes weeks or months for patients to get their results back. The PARS system removes some of those steps.

“[It allows] us to see those same features that are being recovered with chemical staining just by looking at the response to the absorption of light,” PHD candidate and student lead Ben Ecclestone said.

Reza said speeding up the process is important to patients, clinicians and the health care system because of the money, time and stress caused by the current model.

The PARS system has already been used in clinical trials and proved to be highly accurate. The goal is for a prototype to be tested in the field later this year, and then go to market.

“We’d like to deploy this into clinical settings where we could actually start testing this in a real world scenario rather than a lab environment that we’re working in now,” Ecclestone said. Top Stories

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