AI Based X Rays to Predict Covid Infections With 98pc Accuracy

London: Researchers in Scotland have created new X-rays based on artificial intelligence (AI) technology that could eventually replace current PCR testing for identifying COVID-19 infections.

The method, developed by experts at the University of the West of Scotland (UWS), can diagnose COVID-19 with 98 per cent accuracy in just a few minutes, much faster than a PCR test, which typically takes 2 hours.

It is hoped that the technology will one day be used to ease pressure on overburdened emergency rooms, especially in nations where PCR tests are not easily available.

The cutting-edge technique uses X-ray technology to compare scans to a database of roughly 3,000 photos of COVID-19 patients, healthy people, and people with viral pneumonia.

It then makes a diagnosis using an AI method called a deep convolutional neural network, which is normally used to analyse visual imagery.

According to the researchers, the technique proved to be more than 98 per cent accurate during extensive testing.

Professor Naeem Ramzan, Director of the Affective and Human Computing for SMART Environments Research Centre at UWS, 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."

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"However, it can still play an important role in curtailing the virus' spread, especially when PCR tests are not readily available," Ramzan said.

When it comes to diagnosing severe cases of the virus and determining what therapy is needed, X-rays could be important and perhaps life-saving.

The researchers now intend to expand the study by including a larger database of X-ray images acquired by other brands of X-ray equipment to assess the approach's applicability in other medical settings.