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New AI tool may help diagnose post-Covid lung problems

By IANS | Updated: May 24, 2022 15:35 IST

Riyadh, May 24 A novel Artificial Intelligence (AI)-aided diagnostic tool could help overcome some of the challenges of ...

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Riyadh, May 24 A novel Artificial Intelligence (AI)-aided diagnostic tool could help overcome some of the challenges of monitoring lung health following viral infection, researchers have found.

Like other respiratory illnesses, Covid-19 can cause lasting harm to the lungs, but doctors have struggled to visualise this damage.

Conventional chest scans do not reliably detect signs of lung scarring and other pulmonary abnormalities, which makes it difficult to track the health and recovery of people with persistent breathing problems and other post-Covid complications.

The new method developed by a team at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, known as Deep-Lung Parenchyma-Enhancing (DLPE), overlays AI algorithms on top of standard chest imaging data to reveal otherwise indiscernible visual features indicative of lung dysfunction.

Through DLPE augmentation, "radiologists can discover and analyse novel sub-visual lung lesions", said computer scientist and computational biologist Xin Gao.

"Analysis of these lesions could then help explain patients' respiratory symptoms," allowing for better disease management and treatment, he added.

The method, described in the journal Nature Machine Intelligence, first eliminates any anatomical features not associated with the lung parenchyma - the tissues involved in gas exchange serve as the main sites of Covid-induced damage.

That means removing airways and blood vessels, and then enhancing the pictures of what is left behind to expose lesions that might be missed without the computer's help.

The team demonstrated that the tool could reveal signs of pulmonary fibrosis in Covid long-haulers, thus helping to account for shortness of breath, coughing and other lung troubles. A diagnosis, Gao suggests, that would be impossible with standard CT image analytics.

"With DLPE, for the first time, we proved that long-term CT lesions can explain such symptoms," he said. "Thus, treatments for fibrosis may be very effective at addressing the long-term respiratory complications of Covid-19."

While the DLPE is primarily developed with post-Covid recovery in mind, the team also tested the platform on chest scans taken from people with various other lung problems, including pneumonia, tuberculosis and lung cancer.

The researchers showed how their tool could serve as a broad diagnostic aide for all lung diseases, empowering radiologists to, as Gao puts it, "see the unseen".

Disclaimer: This post has been auto-published from an agency feed without any modifications to the text and has not been reviewed by an editor

Tags: Xian GaoSaudi ArabiaRiyadhNature Machine IntelligenceKing abdullah university of science and technologyKingdom of saudi arabiaKaustEnvironmental science and computer science
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