Parkinson’s disease is a progressive disorder of the central nervous system that affects millions of people worldwide.
Diagnosis is typically based on symptoms like tremors, muscle stiffness and impaired balance — an approach that has significant limitations.
“The issue with that method is that patients usually develop symptoms only after prolonged progression with significant injury to dopamine brain neurons,” said study lead author Maximillian Diaz from the University of Florida in the US.
“This means that we are diagnosing patients late in the disease process,” Diaz added.
In the new study, the research team deployed a type of AI called support vector machine (SVM) learning that has been around since 1989.
Using pictures of the back of the eye from both patients with Parkinson’s disease and control participants, they trained the SVM to detect signs on the images suggestive of disease.
The results indicated that the machine learning networks can classify Parkinson’s disease based on retina vasculature, with the key features being smaller blood vessels.
The proposed methods further support the idea that changes in brain physiology can be observed in the eye.
“The single most important finding of this study was that a brain disease was diagnosed with a basic picture of the eye,” Diaz said.
“This is very different from traditional approaches where to find a problem with the brain you look at different brain images,” Diaz added.
The research team noted that those traditional imaging approaches with MRI, CT and nuclear medicine techniques can be very costly.
In contrast, the new approach uses basic photography with equipment commonly available in eye clinics to get an image.
The images can even be captured by a smartphone with a special lens.
“The approach may also have applications in identifying other diseases that affect the structure of the brain, such as Alzheimer’s disease and multiple sclerosis,” Diaz noted.