A pen with magnetic ink and whose data analyzes artificial intelligence (AI) can serve to detect Parkinson’s disease in its early stages, according to a study published in Nature Chemical Engineering.
The device, tested in a group of 16 individuals, precisely recorded the handwriting signals, which were analyzed by a neuronal network -an artificial intelligence method that uses a network of interconnected nodes to learn and distinguish between complex patterns.
The pen successfully distinguished patients with Parkinson’s with an average accuracy of 96.22%, according to a study headed by the University of California (EU).
The operation mechanism is based on the magnetoelastic effect of its magnetoelastic tip and the dynamic ferrofuid ink movement, the article indicates.
It is estimated that Parkinson’s disease affects almost 10 million people in the world and their diagnosis quickly, accessible and effective is crucial to improve the results of patients, but achieving this goal remains a challenge.
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They create a pen with ia that can detect Parkinson’s disease
As the symptoms of the disease include tremors, the diagnosis is usually based on the observation of the patient’s motor skills, but it is a method that lacks objective standards and usually depends on the clinician’s bias.
Pen data analysis can identify differences in the hand of people with and without the disease and, potentially, it could allow earlier diagnoses.
The movements of the hand during writing can be classified into two types: movements in the air, in which the pen moves between strokes without contact with the surface, and surface movements, in which it comes into contact with the writing surface and experiences pressure, forming primary strokes.
The device, which allows “efficient and scalable production through 3D printing”, could represent a low cost, precise and widely distributable technology with the potential to improve the diagnosis of the disease in large populations and in areas of limited resources, the study indicates.
The authors point out that the tool should be expanded to samples of larger patients and the potential of the tool could be explored to trace the progression of Parkinson’s disease stages.
With EFE information.
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