A recent study used AI to analyze changes in the speech of Parkinson’s patients to better diagnose the disease at an early stage.
Researchers from Kaunas University of Technology (KTU) and the Lithuanian University of Health Sciences (LSMU), tried to identify early symptoms of Parkinson’s disease using voice data and found that a change in speech was often the first detectable symptom.
Parkinson’s disease is usually associated with loss of motor function – hand tremors, muscle stiffness, or balance problems.
According to Rytis 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,” said Maskeliūnas.
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, 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,” said Maskeliūnas.
The link between Parkinson’s disease and speech abnormalities is not new to the world of digital signal analysis, and it has been known and researched since the 1960s. However, as technology advances, extracting more information from speech is becoming possible.
In their study, the researchers used artificial intelligence (AI) to analyze and assess speech signals, where calculations and diagnoses are made in seconds rather than hours.
The study is also unique because the results were tailored to the specifics of the Lithuanian language, expanding the AI language database.
“So far, our approach is able to distinguish Parkinson’s from healthy people using a speech sample,” said Kipras Pribuišis, lecturer at the Department of Ear, Nose, and Throat at the LSMU Faculty of Medicine. “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 the recordings. The researchers highlight that the algorithm does not require powerful hardware and could be transferred to a mobile app.
“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,” said Maskeliūnas.
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. Researchers will also check whether the algorithm works well in environments outside of a laboratory setting.