A groundbreaking study indicates that artificial intelligence (AI)-driven eye scans might hold the key to detecting Parkinson’s disease in individuals before they exhibit symptoms.
Collaborating between London’s Moorfields Eye Hospital and the UCL Institute of Ophthalmology, researchers harnessed AI to analyze a comprehensive dataset and identify retinal markers associated with the disease.
By scrutinizing the eyes of those with Parkinson’s and comparing them to individuals without the condition, the AI-powered process unveiled discernible physical distinctions.
This innovative approach offers the potential to serve as a pre-screening tool for early disease detection.
The study centered on data extracted from optical coherence tomography (OCT), a type of 3D scan providing detailed cross-sectional images of the retina. The dataset consisted of 154,830 patients aged 40 and over, who visited eye hospitals in London between 2008 and 2018.
A separate assessment involved a medical database encompassing 67,311 healthy volunteers aged 40 to 69.
Remarkably, the study unveiled that individuals with Parkinson’s exhibited a thinner ganglion cell-inner plexiform layer and inner nuclear layer in their eyes. Notably, these markers were identified approximately seven years prior to the manifestation of clinical symptoms on average.
OCT scans, widely utilized by opticians, offer valuable insights into eye health by visualizing layers of cells beneath the skin’s surface.
Researchers suggest that by scrutinizing these layers in the years preceding symptom onset, it could be possible to detect Parkinson’s disease at an earlier stage, potentially allowing for more timely intervention and management.
