AI gives diagnosis of Covid-19 in 'a few minutes'

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Covid-19 can be detected in a few minutes thanks to Artificial Intelligence (AI) technology developed at University of the West of Scotland. 

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The programme is an improvement on PCR tests that typically take around two-hours.

It is hoped that the technology can eventually be used to help relieve strain on Accident and Emergency departments, particularly in countries where PCR tests are not readily available.

The technique utilises x-ray technology, comparing scans to a database of around 3000 images, belonging to patients with Covid-19, healthy individuals and people with viral pneumonia.

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It then uses a deep convolutional neural network, an algorithm typically used to analyse visual imagery, to make a diagnosis. During an extensive testing phase, the technique proved to be over 98 per cent accurate.

Professor Naeem Ramzan, director of the Affective and Human Computing for SMART Environments Research Centre at UWS, led the team behind the project.

He said: “There has long been a need for a quick and reliable tool that can detect Covid-19, and this has become even more true with the upswing of the Omicron variant.

“Several countries are unable to carry out large numbers of Covid tests because of limited diagnosis tools, but this technique utilises easily accessible technology to quickly detect the virus.

“Covid-19 symptoms are not visible in x-rays during the early stages of infection, so it is important to note that the technology cannot fully replace PCR tests.

“However, it can still play an important role in curtailing the viruses spread especially when PCR tests are not readily available.

“It could prove to be crucial, and potentially life-saving, when diagnosing severe cases of the virus, helping determine what treatment may be required.”

The team – including researchers from Durham University and Northern Border University, Saudi Arabia - plans to expand the study, incorporating a greater database of x-ray images acquired by different models of x-ray machines, to evaluate the suitability of the approach in a clinical setting.

Published in Sensors, the team’s findings can be found here.