AI Helps Diagnose Post-COVID Lung Problems

A new computer-aided diagnostic tool developed by KAUST (King Abdullah University of Science & Technologym, Saudi Arabia) scientists could help overcome some of the challenges of monitoring lung health following viral infection.

Like other respiratory illnesses, COVID-19 can cause lasting harm to the lungs, but doctors have struggled to visualize 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 KAUST - known as Deep-Lung Parenchyma-Enhancing (DLPE) - overlays artificial intelligence 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 analyze novel sub-visual lung lesions," says 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 adds.

Gao and members of his Structural and Functional Bioinformatics Group and the Computational Bioscience Research Center created the tool, along with artificial intelligence researcher and current KAUST Provost Lawrence Carin and clinical collaborators from Harbin Medical University in China.

The method first eliminates any anatomical features not associated with the lung parenchyma; the tissues involved in gas exchange serve as the main sites of COVID-19 - 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 researchers trained and validated their algorithms using computed tomography (CT) chest scans from thousands of people hospitalized with COVID-19 in China. They refined the method with input from expert radiologists and then applied DLPE in a prospective fashion for dozens of COVID-19 survivors with lung problems, all of whom had experienced severe disease requiring intensive care treatment.

In this way, Gao and his colleagues 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, he 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 says. "Thus, treatments for fibrosis may be very effective at addressing the long-term respiratory complications of COVID-19."

Although the KAUST team developed DLPE primarily with post-COVID recovery in mind, they 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."

Zhou L, Meng X, Huang Y et al.
An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors.
Nat Mach Intell, 2022. doi: 10.1038/s42256-022-00483-7

Most Popular Now

Philips and Medtronic Advocacy Partnersh…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, and Medtronic Neurovascular, a leading innovator in neurovascular therapies, today announced a strategic advocacy partnership. Delivering timely stroke...

New AI Tool Predicts Protein-Protein Int…

Scientists from Cleveland Clinic and Cornell University have designed a publicly-available software and web database to break down barriers to identifying key protein-protein interactions to treat with medication. The computational tool...

AI for Real-Rime, Patient-Focused Insigh…

A picture may be worth a thousand words, but still... they both have a lot of work to do to catch up to BiomedGPT. Covered recently in the prestigious journal Nature...

New Research Shows Promise and Limitatio…

Published in JAMA Network Open, a collaborative team of researchers from the University of Minnesota Medical School, Stanford University, Beth Israel Deaconess Medical Center and the University of Virginia studied...

G-Cloud 14 Makes it Easier for NHS to Bu…

NHS organisations will be able to save valuable time and resource in the procurement of technologies that can make a significant difference to patient experience, in the latest iteration of...

Start-Ups will Once Again Have a Starrin…

11 - 14 November 2024, Düsseldorf, Germany. The finalists in the 16th Healthcare Innovation World Cup and the 13th MEDICA START-UP COMPETITION have advanced from around 550 candidates based in 62...

Hampshire Emergency Departments Digitise…

Emergency departments in three hospitals across Hampshire Hospitals NHS Foundation Trust have deployed Alcidion's Miya Emergency, digitising paper processes, saving clinical teams time, automating tasks, and providing trust-wide visibility of...

MEDICA HEALTH IT FORUM: Success in Maste…

11 - 14 November 2024, Düsseldorf, Germany. How can innovations help to master the great challenges and demands with which healthcare is confronted across international borders? This central question will be...

A "Chemical ChatGPT" for New M…

Researchers from the University of Bonn have trained an AI process to predict potential active ingredients with special properties. Therefore, they derived a chemical language model - a kind of...

Siemens Healthineers co-leads EU Project…

Siemens Healthineers is joining forces with more than 20 industry and public partners, including seven leading stroke hospitals, to improve stroke management for patients all over Europe. With a total...

MEDICA and COMPAMED 2024: Shining a Ligh…

11 - 14 November 2024, Düsseldorf, Germany. Christian Grosser, Director Health & Medical Technologies, is looking forward to events getting under way: "From next Monday to Thursday, we will once again...

In 10 Seconds, an AI Model Detects Cance…

Researchers have developed an AI powered model that - in 10 seconds - can determine during surgery if any part of a cancerous brain tumor that could be removed remains...