Lung disease detecting AI could cut winter pressure on the NHS
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Researchers have developed an AI programme that automatically diagnoses lung diseases such as tuberculosis and pneumonia – potentially easing winter pressures on hospitals.
The diseases are potentially serious infections that mainly affect the lungs but often require a combination of different diagnostic tests such as CT scans, blood tests, X-rays, and ultrasounds. These tests can be expensive, and there are often lengthy waiting times for results.
University of the West of Scotland (UWS) researchers originally created an AI to quickly detect Covid-19 from X-ray images. But it has subsequently been proven to automatically identify a range of different lung diseases in a matter of minutes, with a claimed accuracy of around 98 per cent.
The technology could be used to help relieve strain on pressured hospital departments through the quick and accurate detection of disease – freeing up radiographers continuously in high demand; reducing waiting times for test results; and creating efficiencies within the testing process.
UWS researcher Professor Naeem Ramzan said: “Systems such as this could prove to be crucial for busy medical teams worldwide.
“There is no doubt that hospital departments across the globe are under pressure and the outbreak of Covid-19 exacerbated this, adding further strain to pressured departments and staff.
“There is a real need for technology that can help ease some of these pressures and detect a range of different diseases quickly and accurately, helping free up valuable staff time.
“X-ray imaging is a relatively cheap and accessible diagnostic tool that already assists in the diagnosis of various conditions, including pneumonia, tuberculosis and Covid-19. Recent advances in AI have made automated diagnosis using chest X-ray scans a very real prospect in medical settings.”
The technique utilises X-ray technology, comparing scans to a database of thousands of images from patients with pneumonia, tuberculosis and Covid. It then uses a deep convolutional neural network – an algorithm typically used to analyse visual imagery – to make a diagnosis.
Professor Milan Radosavljevic, UWS’s vice-principal of research, said: “Hospitals around the world are under sustained stress. This can be seen throughout the UK, as our fantastic NHS continues to undergo immense pressure, with hard-pressed medical staff bearing the brunt.
“I am excited about the potential of this innovative technology, which could help streamline diagnostic processes and reduce strain on staff.
“It’s another example of purposeful, impactful research at UWS, as we strive to find solutions to global challenges.”
Researchers at UWS are now exploring the suitability of the technology in detecting other diseases using X-ray images, such as cancer.
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