The simple drawing test that could reveal whether you have Parkinson’s
For decades, Parkinson’s disease has been diagnosed using a laborious process of neurological and physical examinations.
But now, researchers in India say the disease could be detected with up to 99 percent accuracy using little more than a drawing test.
The disease is a devastating neurological disorder where neurons become damaged, causing progressively worsening tremors and movement problems that eventually rob patients of their independence.
Suffered by one million Americans, the disease is thought to be rising in the US, which experts have blamed on pollution, pesticides and smoking.
In their research, the team analyzed results from a previous study that involved 66 people, including 31 with Parkinson’s, who were asked to complete two drawing tasks.
In these tests, participants traced spirals and then meanders, or angular-shaped continuous lines. They also held a biometric pen that tracked their hand movements.
Overall, those with Parkinson’s were much less able to trace the lines than those who did not have the condition.
For the new study, the team extracted this data and used it to train a model, which they said could now be used to detect Parkinson’s.
Researchers say that a handwriting trait could be used to detect Parkinson’s disease
In Parkinson’s, the breakdown of neurons can cause symptoms including tremors – movements outside the person’s control – which may leave sufferers unable to hold a pen steady.
Nearly all Parkinson’s patients experience tremors, which may emerge early in the disease or during its later stages.
Other conditions can also cause tremors including hyperthyroidism, low blood sugar, certain medications and withdrawal from substances including alcohol.
The team said in their study: ‘Handwritten images provide spatial characteristics of stroke irregularities, tremor-induced distortions and shape deviations.
‘In contrast, sensor-based handwriting signals capture motor behavior, which includes velocity fluctuations, pressure inconsistencies and coordination.’
In the study, published in the journal Discover Computing, researchers fed the images and data from hand movements into different AI systems.
Each model then evaluated spatial irregularities and differences in motor control and hand coordination between those who did and did not have the condition.
The data from each was then processed into an algorithm called SNAKE, which was then used to re-evaluate each drawing and determine which participants did or did not have Parkinson’s.
Shown above are the spirals, top row, and meanders, bottom row, that were used to test for Parkinson’s. The two drawings on the right are by Parkinson’s patients.
The above shows a meander drawn by a person who does not have Parkinson’s disease (left) and who has Parkinson’s
The above shows a spiral drawn by a person who does not have Parkinson’s disease (left) and does have Parkinson’s
According to the researchers, this algorithm correctly diagnosed Parkinson’s using the meander drawings in 98.95 percent of cases.
When analyzing spatial patterns, it correctly detected Parkinson’s in 97.7 percent of cases.
The researchers said the test could be a less invasive way to diagnose Parkinson’s. It was not clear whether it would also help to detect the disease in the early stages.
It is not clear whether the algorithm may now be used by doctors to help them confirm a Parkinson’s disease diagnosis.
The dataset was small, including 66 participants, and the algorithm was not evaluated using a new group of participants or new drawings.
The researchers, from Siksha ‘O’ Anusandhan University, concluded: ‘This study proposed a multimodal handwriting-based framework for Parkinson’s disease detection.’
