Technology used by engineers to listen for faults in bridges could be used to diagnose "noisy" arthritic knees, a study suggests.
It involved a tiny microphone being attached to participants' knees to pick up high-frequency sounds.
Although not audible to humans, the waveforms can be analysed by computers to give an insight into knee health.
Better diagnosis of osteoarthritis and more tailored treatments are possible, the researchers said.
But they acknowledged that more research and trials in larger numbers of people were needed first.
Osteoarthritis of the knee is a common degenerative joint condition, which can cause pain, stiffness and swelling in the joint.
Normally, the body can repair low-level damage to the joints – but with osteoarthritis, the protective cartilage on the ends of bones breaks down and cannot mend itself.
In this study, involving Lancaster University, the University of Central Lancashire and Manchester University, researchers "listened" to the noise produced by the knees of 89 adults with osteoarthritis.
They were all asked to stand up from a seated position five times while acoustic signals from their joints were recorded.
They assumed the knees would act like engineering structures – with "smooth and well-lubricated surfaces" moving quietly against each other, and "uneven movements of rough, poorly-lubricated surfaces" generating acoustic signals.
Their results showed that the technique could "hear" the difference between the signals produced by healthy knees and those with osteoarthritis.
Prof John Goodacre, from Lancaster University, who led the study, said it was a promising technique.
"The current way of grading knee osteoarthritis is crude, usually involving an X-ray, and the picture can change every few months.
"This is a finer, more sensitive way of grading severity without relying on an X-ray."
Knee tune
The research team, who published their findings in PLOS One, found that the more "hits" that were visible on the waveforms produced by the knees, the more "noisy" the knee anRead More – Source
