3D-printed pen successfully detects Parkinson's disease with 96.22% accuracy
In the U.S. alone, an estimated 1.1 million individuals have Parkinson's disease, making it the second-most common neurodegenerative disease. One of the biggest challenges associated with this disease is that the most significant symptoms only occur in the later stages. Existing early-diagnosis systems are costly and not very accessible.
To tackle this problem head-on, these UCLA researchers led by Jun Chen developed a smart self-powered pen that can detect Parkinson's disease in its early stage through the analysis of handwriting. The smart pen features a magnetoelastic silicon tip, ferrofluid ink, a coil, and a grip.
When the soft tip of the pen moves in the air or is pressed against a surface, there is a change in its magnetic properties, this change in turn affects the movement of the ferrofluid in the barrel. A coil around the section containing the ferrofluid records and transmits the changes as signals. The signals are then analyzed using a trained model, this way the pen is used to diagnose Parkinson's disease.
Detection of subtle motor symptoms unnoticeable to the naked eye is critical for early intervention in Parkinson’s disease. Our diagnostic pen presents an affordable, reliable and accessible tool that is sensitive enough to pick up subtle movements and can be used across large populations and in resource-limited areas. — Jun Chen.
In a proof-of-concept study involving 16 participants, 3 of whom had Parkinson's disease, the model was accurate 96.22% of the time. If this novel approach makes it through clinical trials, the diagnosis of Parkinson's disease could become cheaper and more accessible.
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