This non-invasive machine learning software uses fundus retinal images to predict the onset or presence of Parkinson’s disease (PD). Parkinson’s disease is the second most common neurodegenerative disorder, affecting nearly 9 million people, and involves the decay of dopamine production and movement. The gold standard for diagnosing Parkinson’s is the presence of motor symptoms, including tremors, impaired balance, cardiac arrhythmias, and sleep disorders. However, motor symptoms primarily occur in the later stages of the disease when a patient has lost approximately 80% of dopaminergic cells; there is growing interest in exploring non-motor symptoms and biomarkers associated with the disease.
The retina provides a unique opportunity to study neurodegeneration without directly analyzing the brain. In Parkinson’s patients, dopaminergic cells in the substantia nigra, the region of the brain responsible for dopamine and movement, decay and thin the retinal walls and retina microvasculature. The optic nerves are viewable using standard photography or imaging, such as optical coherence tomography (OCT). While OCT imaging provides three-dimensional structures of the eye, fundus eye images are a more clinically relevant diagnosis tool. Retinal fundus images are simply photographs of the back of the eye, including the blood vessels, retina, and optic nerve head, making them highly portable and affordable. It is possible to leverage eye data as a potential means for early Parkinson’s disease diagnosis.
Researchers at the University of Florida have developed machine learning software for identifying the onset or presence of Parkinson's disease by analyzing fundus retinal images. Using a well-trained machine learning algorithm, it identifies vital features in the retinal images, specifically tracking blood vessel patterns, to indicate the presence of Parkinson's in an individual.
Machine learning software analyzes blood vessel patterns in retinal fundus images to diagnose Parkinson’s disease
This non-invasive machine learning software tracks eye blood vessel patterns to diagnose Parkinson’s disease. The software undergoes training using retinal images sourced from two distinct groups: patients previously diagnosed with Parkinson’s and healthy individuals. During the training, the system learns to classify features within the retinal images associated with the two groups. Once training is complete, the system can analyze new retinal images from human subjects. It can detect various retinal features within the retinal image, such as eye blood vessel maps, and predict whether the image indicates an onset or presence of Parkinson’s disease in a patient.
