Media release

From: Springer Nature

Biomedical engineering: AI pen can detect Parkinson’s disease from handwriting

A pen loaded with magnetic ink can be used to help detect early onset Parkinson’s disease, suggests a study published in Nature Chemical Engineering. Neural network assisted data analysis in the device can identify differences in the handwriting of people with and without the disease and could potentially allow for earlier diagnoses.

Parkinson’s disease is estimated to affect nearly 10 million people around the globe and is the second most common neurodegenerative disease after Alzheimer’s disease. Parkinson’s disease is also the fastest-growing neurodegenerative disease around the world — and diagnoses are thought to be underestimated in low- and middle-income countries due, in part, to a shortage of medical specialists trained to diagnose the disease in these countries. Since symptoms of the disease include tremors, diagnosis is typically based on observing patient motor skills. However, this method lacks objective standards and is typically dependent on the bias of the clinician.

Jun Chen and colleagues developed an approach to diagnosing Parkinson’s disease from handwriting samples taken with a custom-built pen containing magnetic ink. By converting the movements of the magnetic ink to electrical signals from writing on a surface and in the air, the authors demonstrate that with the assistance of a neural network — a method in artificial intelligence that uses a web of interconnected nodes to learn and distinguish between complex patterns — the pen can successfully distinguish the handwriting of patients with Parkinson’s disease from those without the disease with more than 95% accuracy in a small-scale cohort of 16 individuals.

This diagnostic pen could represent a low-cost, accurate and widely distributable technology with the potential to improve Parkinson’s disease diagnostics across large populations and in resource-limited areas. The authors note that future work should expand the tool to larger patient samples and could explore the potential of the tool to track the progression of Parkinson’s disease stages.