A new screening test application could help advance the early detection of Parkinson’s disease and severe COVID-19, improving the management of these illnesses. The test was developed by a research team of engineers and neurologists led by RMIT University and can produce accurate results using just people’s voice recordings.
Across the globe, millions of people have Parkinson’s, a degenerative brain condition that can be challenging to diagnose as symptoms vary among people. Common symptoms include slow movement, tremors, rigidity and imbalance. Currently, Parkinson’s is diagnosed via an evaluation by a neurologist that can take up to 90 minutes.
Powered by artificial intelligence (AI), the smartphone application records a person’s voice and takes just 10 seconds to reveal whether they may have Parkinson’s disease and should be referred to a neurologist.
The lead researcher on the project, Professor Dinesh Kumar, from the School of Engineering, note that the easy-to-use screening test made it ideal to use in a national screening program. The team developed a similar test for people with COVID-19 to reveal whether they need clinical attention, including hospitalisation.
Prof. Kumar noted that early detection, diagnosis and treatment could help manage these illnesses, so making screening faster and more accessible is critical. This research will allow a non-contact, easy-to-use and low-cost test that can be performed routinely anywhere in the world, where the clinicians can monitor their patients remotely. The technology could also promote a community-wide screening program, reaching people who might not otherwise seek treatment until it’s too late.
How the technology works
The voice of people with Parkinson’s disease gets altered as a result of a combination of three symptoms: rigidity, tremor and slowness (known as bradykinesia). Expert clinicians can identify these symptoms, but this assessment can be challenging due to the large natural differences in people’s voices.
Previous attempts to develop a computerised voice assessment to detect Parkinson’s were inaccurate due to these significant differences in people’s voices. As part of their research, the team utilised the voice recordings of people with Parkinson’s and a controlled group of so-called healthy people saying three sounds – A, O and M. These sounds result in more accurate detection of the disease.
In patients with pulmonary disease symptoms from COVID-19, there is also a change in the voice due to lung infection, Prof. Kumar said. Again, the large differences in people’s voices mean that pulmonary disease is difficult to recognise in its early stages.
The team overcame this limitation with the choice of those same three sounds and the AI method of analysis we’ve developed. Before being used, the system is trained to identify the disease. Once trained, it performs an instantaneous analysis of the voice. The software then compares the results against existing samples of voices of people with Parkinson’s against those who do not.
A co-researcher on the project, Dr Quoc Cuong Ngo, from the School of Engineering, said the new technology was faster and better than any similar AI-based approach. He noted that the screening test app can measure, with great precision, how the voice of someone with Parkinson’s disease or a person at high risk of hospitalisation from COVID-19 is different from healthy people.
Looking ahead
The team aims to perform a larger, observational study to detect the progression of Parkinson’s and pulmonary diseases. They are also keen to test the efficacy of this technology for other diseases, such as other neurological conditions and sleep disorders.
The team hopes to identify a suitable commercial partner and clinical partner ahead of a clinical trial planned for next year.
The researchers from RMIT partnered with the Technical University of Košice in Slovakia, the University of Surabaya in Indonesia and the Rajshahi University of Engineering and Technology in Bangladesh on this work. The research results have been published in several peer-reviewed journals.