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Scientists in Scotland have developed an artificial intelligence (AI) based test that uses X-rays to accurately diagnose Covid in just a few minutes.
The testing platform developed by researchers at the University of the West of Scotland (UWS) is able to detect the SARS-CoV-2 virus far more quickly than a PCR test which typically takes around two hours.
The technology can eventually be used to help relieve strain on hard-pressed hospitals, particularly in countries where PCR tests are not readily available, they said.
The technique utilises X-ray technology, comparing scans to a database of around 3000 images belonging to patients with Covid, healthy individuals and people with viral pneumonia.
It then uses an AI process known as deep convolutional neural network, an algorithm typically used to analyse visual imagery, to make a diagnosis.
According to the research published in the journal Sensors, the technique proved to be more than 98 per cent accurate during an extensive testing phase.
"There has long been a need for a quick and reliable tool that can detect Covid, and this has become even more true with the upswing of the Omicron variant," said Professor Naeem Ramzan from UWS, who led the research.
"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," Ramzan said.
The researchers noted that Covid symptoms are not visible in X-rays during the early stages of infection, so 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, they said.
"It could prove to be crucial, and potentially life-saving, when diagnosing severe cases of the virus, helping determine what treatment may be required," Ramzan said.
The team now 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.
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