AI imaging tech diagnoses COVID-19 in minutes


Tuesday, 25 January, 2022

AI imaging tech diagnoses COVID-19 in minutes

Pioneering artificial intelligence (AI) technology, developed at University of the West of Scotland (UWS), has been found capable of accurately diagnosing COVID-19 in just a few minutes — far more quickly than PCR tests, which typically take at least two hours. It is hoped that the technology could eventually be used to help relieve strain on hard-pressed accident and emergency departments, particularly in countries where PCR tests are not readily available.

Utilising X-ray technology, the new technique compares scans to a database of around 3000 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% accurate.

The project was led by Professor Naeem Ramzan, Director of the Centre for Affective and Human Computing for SMART Environments at UWS, and published in the journal Sensors. Prof Ramzan 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.”

Prof Ramzan noted that COVID-19 symptoms are not visible in X-rays during the early stages of infection, so the technology cannot fully replace PCR tests. “However,” he said, “it can still play an important role in curtailing the virus’s spread, especially when PCR tests are not readily 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.”

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

Image caption: Sample COVID-19 chest X-ray from the used dataset. Image courtesy of the study authors (cropped from the original) under CC BY 4.0

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