According to the study published in the journal Neurology, researchers were able to demonstrate how disease-related markers were identified years before clinical diagnosis.
Researchers from London’s Moorfields Eye Hospital and the UCL Institute of Ophthalmology used AI and machine learning to identify markers of the disease in eye scans.
The procedure discovered physical differences in the eyes of people with Parkinson’s disease and those who did not have the condition.
The researchers studied data collected from 154,830 patients over the age of 40 who attended eye hospitals in London between 2008 and 2018, using optical coherence tomography (OCT), a type of 3D scan that produces a detailed image of the cross-section of the retina.
They discovered that Parkinson’s patients had a thinner ganglion cell-inner plexiform layer and inner nuclear layer in their eyes. It discovered these markers seven years before clinical presentation, on average.
OCT scans, which are commonly used by opticians, are beneficial for monitoring eye health because they reveal layers of cells beneath the skin’s surface.
According to the researchers, looking at these layers in the years before symptoms present themselves could help detect the disease earlier.
“While we are not yet ready to predict whether an individual will develop Parkinson’s, we hope that this method could soon become a pre-screening tool for people at risk of disease,” said lead author Dr Siegfried Wagner from the UCL Institute of Ophthalmology and Moorfields Eye Hospital.
Parkinson’s disease is a progressive neurological condition in which there is insufficient dopamine in the brain, resulting in problems that worsen over time.
The most common symptoms are involuntary shaking of various parts of the body, slow movement, and stiff and inflexible muscles, but there may also be psychological symptoms such as depression, loss of smell, and memory problems.