UBCO develops alternate COVID-19 testing method

Alternate COVID-19 test?

University of British Columbia Okanagan researchers believe they may have found an alternate screening method for COVID-19.

The polymerase chain reaction (PCR) test is used to detect genetic material from a specific organism, such as a virus, and it's considered the gold-standard for COVID-19 tests in Canada.

But not all parts of the world have access to PCR tests and there is a need to rapidly screen patients for COVID-19.

UBCO believes they have found one.

The group of researchers have developed CORONA-Net, a deep-learning neural network that can quickly detect COVID-19 infections using X-ray images.

“Doctors around the world need a way to rapidly test patients for COVID-19 so that they can begin immediate treatment for patients with the virus," says Dr. Mohamed S. Shehata.

“The PCR method has some drawbacks, including longer detection time and lower detection rate of the virus. According to recommendations by the World Health Organization provided in October 2020, chest imaging examination is an effective method for detecting clinical symptoms of people who have been affected by and recovered from the virus.”

Dr. Shehata explains that in many countries, people opt for a chest X-ray because of the cost of a PCR test or its unavailability. The UBCO method using CORONA-Net, means the artificial intelligence system can flag suspicious cases to be fast-tracked and looked at quickly.

"COVID-19 typically causes pneumonia in human lungs, which can be detected in X-ray images. These datasets of X-rays—of people with pneumonia inflicted by COVID-19, of people with pneumonia inflicted by other diseases, as well as X-rays of healthy people—allow the possibility to create deep learning networks that can differentiate between images of people with COVID-19 and people who do not have the disease,” said graduate student Sherif Elbishlawi.

So far when it comes to detecting COVID-19, CORONA-Net has been able to produce results with an accuracy of more than 95 per cent from digital chest X-ray images and researchers say the accuracy will increase as the dataset grows. According to Elbishlawi, the program can automatically improve itself over time and self-learn to be more accurate.

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