The expertise will help relieve pressure on hard-pressed hospitals, notably in nations the place PCR assessments.
The approach makes use of X-ray expertise, evaluating scans to a database of round 3000 pictures belonging to sufferers with COVID-19, wholesome people, and folks with viral pneumonia.
It used an AI course of often known as deep convolutional neural community; an algorithm usually used to investigate visible imagery to make a prognosis.
According to the analysis revealed within the journal Sensors, the approach proved to be greater than 98 per cent correct throughout an intensive testing phase.
“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,” mentioned Professor Naeem Ramzan from UWS, who led the analysis.
“Several countries are unable to carry out large numbers of COVID-19 tests because of limited diagnosis tools, but this technique utilizes easily accessible technology to detect the virus quickly,” Ramzan mentioned.
The researchers famous that COVID-19 signs are usually not seen in X-rays throughout the early phases of an infection, so the expertise can not absolutely change PCR assessments.
However, it could nonetheless play an necessary position in curbing the unfold of the virus, particularly when PCR assessments are usually not available.
“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 mentioned.
The group now plans to increase the research, incorporating a higher database of X-ray pictures acquired by totally different X-ray machines fashions to judge the method’s suitability in a scientific setting