Covid-19: AI-based X-rays with 98 per cent accuracy may replace PCR tests

This picture has been used for representational purpose

Scottish researchers have developed new Artificial Intelligence (AI) technology-based X-rays that can likely replace currently used PCR tests for detecting Covid-19 infections.

The technology developed by experts at the University of the West of Scotland (UWS), is capable of accurately diagnosing Covid-19 in just a few minutes -- far more quickly than a PCR test, which typically takes around 2 hours -- and with 98 per cent accuracy.

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

The state-of-the-art 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 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 more than 98 per cent accurate, the researchers 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," said Professor Naeem Ramzan, Director of the Affective and Human Computing for SMART Environments Research Centre at UWS.

"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," Ramzan said.

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

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