The magic pen with AI that detects Parkinson's early

A pen loaded with magnetic ink could detect Parkinson's disease in its early stages, suggests a study published in Nature Chemical Engineering .

The key is how you interpret a person's writing through their writing.

The device is capable of performing neural network-assisted data analysis that can identify differences in the handwriting of people with and without the disease and could allow for earlier diagnoses.

Jun Chen's team at the University of California (USA) has designed a method to diagnose Parkinson's disease from handwriting samples taken with a customized pen containing magnetic ink.

By converting the movements of magnetic ink into electrical signals from writing on a surface and in the air, the authors demonstrate that, with the help of a neural network— an artificial intelligence method that uses a network of interconnected nodes to learn and distinguish between complex patterns —the pen can successfully distinguish the handwriting of patients with Parkinson’s disease from that of people without the disease with over 95% accuracy in a small-scale cohort of 16 individuals.

Parkinson's disease is estimated to affect nearly 10 million people worldwide and is the second most common neurodegenerative disease after Alzheimer's.

Parkinson's disease is also the fastest-growing neurodegenerative disease worldwide, and diagnoses are believed to be underestimated in low- and middle-income countries, due in part to a shortage of specialist physicians trained to diagnose the disease. Since symptoms of the disease include tremors, diagnosis is typically based on observing the patient's motor skills.

However, this method lacks objective criteria and often relies on physician bias.

According to its authors, this diagnostic pen could represent a low-cost, accurate, and widely distributable technology , with the potential to improve the diagnosis of Parkinson's disease in large populations and in areas with limited resources.

The researchers note that future work should expand the tool to larger patient samples and could explore the tool's potential to track the progression of Parkinson's disease stages.