Researchers attempted to identify early symptoms of Parkinson's disease using voice data. In their study, the researchers used artificial intelligence (AI) to analyze and assess speech signals, where calculations are done and diagnoses made in seconds rather than hours.
From: Kaunas University of Technology [in Lithuania]
January 24, 2023 -- The
diagnosis of Parkinson's disease has shaken many lives. More than 10 million
people worldwide are living with it. There is no cure, but if symptoms are
noticed early, the disease can be controlled. As Parkinson's disease
progresses, along with other symptoms speech changes.
Lithuanian researcher
from Kaunas University of Technology (KTU), Rytis Maskeliūnas, together with
colleagues from the Lithuanian University of Health Sciences (LSMU), tried to
identify early symptoms of Parkinson's disease using voice data.
Parkinson's disease is
usually associated with loss of motor function -- hand tremors, muscle
stiffness, or balance problems. According to Maskeliūnas, a researcher at KTU's
Department of Multimedia Engineering, as motor activity decreases, so does the
function of the vocal cords, diaphragm, and lungs: "Changes in speech
often occur even earlier than motor function disorders, which is why the
altered speech might be the first sign of the disease."
Expanding the AI
language database
According to Professor
Virgilijus Ulozas, at the Department of Ear, Nose, and Throat at the LSMU
Faculty of Medicine, patients with early-stage of Parkinson's disease, might
speak in a quieter manner, which can also be monotonous, less expressive,
slower, and more fragmented, and this is very difficult to notice by ear. As
the disease progresses, hoarseness, stuttering, slurred pronunciation of words,
and loss of pauses between words can become more apparent.
Taking these symptoms
into account, a joint team of Lithuanian researchers has developed a system to
detect the disease earlier.
"We are not
creating a substitute for a routine examination of the patient -- our method is
designed to facilitate early diagnosis of the disease and to track the
effectiveness of treatment," says KTU researcher Maskeliūnas.
According to him, the
link between Parkinson's disease and speech abnormalities is not new to the
world of digital signal analysis -- it has been known and researched since the
1960s. However, as technology advances, it is becoming possible to extract more
information from speech.
In their study, the
researchers used artificial intelligence (AI) to analyse and assess speech
signals, where calculations are done and diagnoses made in seconds rather than
hours. This study is also unique -- the results are tailored to the specifics
of the Lithuanian language, in this way expanding the AI language database.
The algorithm will
become a mobile app in the future
Speaking about the
progress of the study, Kipras Pribuišis, lecturer at the Department of Ear,
Nose, and Throat at the LSMU Faculty of Medicine, emphasises that it was only
carried out on patients already diagnosed with Parkinson's: "So far, our
approach is able to distinguish Parkinson's from healthy people using a speech
sample. This algorithm is also more accurate than previously proposed."
In a soundproof booth,
a microphone was used to record the speech of healthy and Parkinson's patients,
and an artificial intelligence algorithm "learned" to perform signal
processing by evaluating these recordings. The researchers highlight that the
algorithm does not require powerful hardware and could be transferred to a
mobile app in the future.
"Our results,
which have already been published, have a very high scientific potential. Sure,
there is still a long and challenging way to go before it can be applied in
everyday clinical practice," says Maskeliūnas.
According to the
researcher, the next steps include increasing the number of patients to gather
more data and determining whether the proposed algorithm is superior to
alternative methods used for early diagnosis of Parkinson's. In addition, it
will be necessary to check whether the algorithm works well not only in
laboratory-like environments but also in the doctor's office or in the
patient's home.
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