AI tech helps diagnose Covid-19 ‘in minutes’

The method, developed by a team at the University of the West of Scotland (UWS), can detect the virus far more quickly than a PCR test, which typically takes around  two hours.

It is hoped that the technology can eventually help relieve strain on hard-pressed A&E departments, particularly in countries where PCR tests are not readily available.

The technique utilises X-ray technology, comparing scans to a database of around 3,000 images belonging to patients with Covid-19, healthy individuals, and people with viral pneumonia.

It then uses an AI process known as 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 over 98 per cent accurate, according to the researchers.

“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,” said Professor Naeem Ramzan, director of the Affective and Human Computing for SMART Environments Research Centre at UWS.

Ramzan explained that several countries cannot carry out large numbers of Covid tests because of limited diagnosis tools, but this technique uses easily accessible technology to 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,” he added. “But it can still play an important role in curtailing the virus's spread, especially when PCR tests are not readily available.”

Ramzan said the technology could prove crucial, and potentially life-saving, when diagnosing severe cases of Covid-19, helping to determine what treatment may be required.

Professor Milan Radosavljevic, vice-principal of research, innovation and engagement at UWS, added: “This is potentially game-changing research. It’s another example of the purposeful, impactful work that has gone on at UWS throughout the pandemic, making a genuine difference in the fight against Covid-19.”

The team now plans to expand the study, incorporating a greater database of images gained by different models of X-ray machines, to evaluate the suitability of the approach in a clinical setting.