The Neural Impairment Test Suite app created by researchers at the Kaunas University of Technology (KTU) in Lithuania provides its user with a series of tests in order to check the presence of the symptoms.
If the probability of symptoms is detected, the user is informed and encouraged to contact medical professionals for further advice.
If a parent has the gene, each son or daughter has a one in two (50/50) chance of inheriting the disease.
Adult-onset Huntington’s disease, the most common form of this disorder, usually appears in a person’s thirties or forties.
Early signs and symptoms of the disease can include irritability, depression, small involuntary movements, poor coordination, and trouble learning new information or making decisions. All these are difficult to notice.
“Our app is aimed at the early detection — we are attempting to diagnose the disease when visually there are no symptoms,” said Andrius Lauraitis, KTU doctoral student.
The app is one-of-a-kind, as the technological devices in the context of this disease have not been investigated yet.
“Due to the hereditary nature of the disease a person might know that he or she is in a risk group, but it is not known when and if the disease will strike,” Maskeliunas said.
“When the early symptoms are detected, the person is advised to contact a physician. Although there is no known treatment for Huntington’s disease, it is estimated that a patient can gain 3-16 years of healthy life if the disease is diagnosed early,” said Maskeliunas.
The app is a collection of various tests available to smartphone users on Google Play. The tasks on the app are designed to evaluate the user’s motor, cognitive skills, to detect voice and energy consumption disorders.
According to KTU scientists, usually in medical practice similar diagnostic tests are provided on paper, but this is the first attempt to digitalise the instrument.
Depending on the degree of risk of developing the disease, the user can take the test once a week or more often; his or her data is being stored in the user’s profile.
The model developed by KTU scientists is counting the values and predictions according to the indicators of the progress of the disease.
“For example, the test of a 3D figure construction (a cube or a cuboid) is digitalised with the application of graph theory. The algorithm is comparing two graphs — one exemplary and one formed by the patient – in order to evaluate the metrics of similarity,” said Lauraitis.
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