New hi-tech AI tool cuts time to diagnose lung diseases

RESEARCHERS believe that developing cutting-edge artificial intelligence (AI) that can quickly and accurately identify lung diseases like pneumonia and tuberculosis could relieve the strain that winter months place on hospitals.

Tuberculosis and pneumonia – potentially serious infections which mainly affect the lungs – often require a combination of different diagnostic tests, such as CT scans, blood tests, X-rays, and ultrasounds. These tests can be expensive, with often lengthy waiting times for results.

Developed by UWS, the revolutionary technology – originally created to quickly detect Covid-19 from X-ray images – has been proven to automatically identify a range of different lung diseases in a matter of minutes, with around 98% accuracy.

UWS researcher Professor Naeem Ramzan said: “Systems such as this could prove to be crucial for busy medical teams worldwide.”

It is hoped that the technology can be used to help relieve strain on pressured hospitals 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.

Professor Ramzan, director of the Affective and Human Computing for SMART Environments Research Centre at UWS, led the development of the technology, along with UWS PhD students Gabriel Okolo and Dr Stamos Katsigiannis.

Ramzan added: “No doubt, hospital departments across the globe are under pressure. Covid-19 exacerbated this, adding further strain to pressured departments and staff. There is a real need for technology to ease the 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 in the diagnosis of pneumonia, tuberculosis and Covid-19. Recent advances in AI have made automated diagnosis using chest X-ray scans a very real prospect.”

This state-of-the-art 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 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 98% accurate.

Professor Milan Radosavljevic, UWS’s Vice-Principal of Research and Innovation, said: “Hospitals around the world are under sustained stress, as seen throughout the UK with hardpressed 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.