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

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

Scottish researchers have developed new Artificial Intelligence (AI) technology-based X-rays that could potentially replace PCR tests currently used to detect COVID-19 infections.

The technology developed by experts from the University of the West of Scotland (UWS) is capable of accurately diagnosing COVID-19 in minutes – far faster than a PCR test, which usually takes about 2 hours – and with 98 percent accuracy.

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

Using state-of-the-art X-ray technology, the scans are compared to a database of nearly 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 commonly used to analyze visual imagery, to make diagnoses.

During an extensive testing phase, the technique proved to be more than 98 percent accurate, the researchers said.

Professor Naeem Ramzan, Director of Affective and Human Computing for SMART, said, “There has long been a need for a quick and reliable tool that can detect COVID-19, and this became even more true with the advancement of the Omron version Is.” Environmental Research Center at UWS.

“Many countries are unable to conduct large numbers of COVID tests due to limited diagnostic equipment, but this technology uses easily accessible technology to quickly detect the virus.

“Covid-19 symptoms are not visible on X-rays during the early stages of infection, so it is important to note that the technique cannot completely replace PCR tests.

“However, it can still play an important role in containing the spread of the virus, especially when PCR tests are not readily available,” Ramzan said.

X-rays can prove to be important and potentially life-saving when diagnosing severe cases of the virus, helping to determine what treatment may be needed. The team now plans to expand the study to include a larger database of X-ray images obtained by different models of X-ray machines to evaluate the suitability of the approach in a clinical setting.

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