Wireless sensing technology that captures the reflection of microwave signals from patients’ chests could help detect a range of lung conditions according to a new study.
University of Glasgow engineers and computer scientists have teamed up with colleagues at a hospital in Lahore, Pakistan, in a potential breakthrough for new forms of touch-free diagnostics.
They say their findings could lead to new forms of personalised health monitoring both in clinical settings and in the ‘smart homes’ of the future.
The system, showcased in a new paper published in the journal Communications Medicine, uses radio signals of multiple frequencies paired with sophisticated artificial intelligence to recognise the characteristic breathing patterns of five common lung diseases.
It works by exposing patients to harmless microwave signals emitted by a pair of software-defined radios at 5.23Ghz – a frequency at the lower end of the bands expected to be used to future 6G and WiFi7 networks.
AI-enabled analysis of the signals reflected from the patients’ chests allows the system to identify the breathing patterns which are caused by different lung disorders.
In lab tests using real-world radio data, the system was able to accurately screen for asthma, chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), pneumonia and tuberculosis with 98% accuracy.
Professor Qammer H. Abbasi, director of the University of Glasgow’s Centre for Integrated Sensing and Communication Enabling Cognitive Cities, led the research and is one of the paper’s corresponding authors.
He said: “The ultrafast 6G wireless communications networks of the future have the potential to do integrated sensing and communication (ISAC), which will unlock a wide range of benefits for people around the world, with healthcare being one of the key applications.
“This research showcases the effectiveness of ISAC, which allows a single communications infrastructure to both transmit data and perform sensing tasks at the same time. The sophisticated sensing which underpins our results only took up 12.5% of the system’s available bandwidth. That means that the rest of the system’s bandwidth could be used for data transmission to help enable future generations of integrated, continuous health monitoring devices.”
Professor Muhammad Mahboob Ur Rahman, of the Information Technology University in Pakistan, is another of the paper’s corresponding authors.
He said: “Being able to do accurate, low-cost, and swift mass screening of people for their respiratory health in a non-clinical and resource-constrained setting, without requiring them take invasive tests such as spirometry or uncomfortable radiations such as X-rays and CT scans could be a game-changer for healthcare delivery, especially amidst the outbreak of a pandemic.
“By combining AI with radio sensing in a 6G/WiFi framework, we’ve been able to accurately spot the signs of lung disease without the need for physical contact, stethoscopes, or imaging scans. That could help enable safe, continuous, and contactless screening and early detection of anomalies, which has the potential to reduce healthcare costs due to early medical intervention. This work is also anticipated to have a big impact in low-resource settings and during future outbreaks of infectious diseases like COVID-19, where reducing contact with patients could help limit the spread of infections.”
