Parkinson鈥檚 disease lacks a conclusive test, so it is generally diagnosed by assessing symptoms. But now, scientists have shown that AI models can identify signs of the condition in a person鈥檚 voice with more than 90 per cent accuracy, and possibly before the onset of any movement-related issues.
Parkinson鈥檚 is characterised by the proliferation of a misfolded form of a protein called alpha-synuclein. It has been suggested that tests could look for clumps of this protein in people鈥檚 spinal fluid or in .
Looking for a low-cost, non-invasive alternative, at the University of North Texas and his colleagues collected 195 voice recordings from 31 people, 23 of whom had been diagnosed with Parkinson鈥檚. Some of these recordings were then used to train four AI models to detect the condition, based on vocal features such as hoarseness and an irregular pitch.
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Up to 90 per cent of people with Parkinson鈥檚 disease develop dysarthria, defined as difficulty speaking because the muscles used for speech are weak, says at Binghamton University in New York state, who wasn鈥檛 involved in the study.
After the models were trained, different voice recordings from the participants were used to validate them. When put to the test on the remaining recordings, the models demonstrated more than 90 per cent accuracy at identifying people with Parkinson鈥檚 disease.
Dysarthria seems to be caused by , and in Parkinson鈥檚 progression, says Bishop, which makes such vocal changes 鈥渁n intriguing early marker for Parkinson鈥檚 disease鈥�.
The voice-based approach 鈥渟hows real promise as an early screening tool鈥�, says at Rune Labs, a software and data analytics company for precision neurology in California. 鈥淏ecause it only requires a microphone and an internet connection, this method could be used remotely, at scale, even in places without easy access to neurologists.鈥�
But Arnold cautions that the dataset used in this study was small. Larger ones, made up of voice recordings with a range of accents, are needed before it can be used as a standard diagnostic, he says.
medRxiv
