Webcams, AI track Parkinson’s progression using eye movement
Neuralight says trial shows its system outperforms traditional tests
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- Webcams and AI analysis track Parkinson's progression via eye movements.
- This system is more sensitive than traditional tests tracking disease changes.
- The company sais it offers a scalable method for monitoring in clinical research.
Neuralight said a newly completed study showed its machine learning-based system monitoring eye movements via webcam is more sensitive than traditional tests used for tracking Parkinson’s disease progression.
“For the first time, a biomarker has outperformed the neurological gold standard in tracking Parkinson’s disease over time, unlocking a new standard in neurology,” Edmund Ben-Ami, Neuralight’s co-founder and CEO, said in a company press release.
The PALOMA study (NCT05862649) enrolled 300 adults with Parkinson’s followed for one year. Initial data from the study demonstrated that the biomarkers derived from the test line up with those of traditional measures of Parkinson’s progression. Results also showed Neuralight’s eye-movement biomarkers were 10 times more sensitive than the most commonly used measure, the Movement Disorder Society-Sponsored Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), the company said.
“PALOMA is an important step toward more objective, scalable ways to track Parkinson’s disease progression in real-world clinical research,” said Michelle Tosin, PhD, the trial’s leading principal investigator and an assistant professor at Rush University Medical Center. “By comparing NeuraLight’s precise eye-movement measures with standard clinical outcome assessments … we can capture consistent signs of change over time.”
Parkinson’s is caused by the progressive loss of certain nerve cells called dopaminergic neurons, which disrupts normal brain signaling and leads to Parkinson’s symptoms. A significant proportion of patients have slower and less accurate eye movements, which tend to worsen as the disease progresses. This suggests that measuring differences in eye movements may aid in diagnosing Parkinson’s and tracking its progression.
Neuralight’s computer-based oculometric test tracks eye movements through facial videos captured with a standard webcam and analyzes them using machine learning techniques.
Short exam time ‘supports repeat use’ in studies
A previous study (NCT05437003) using the Neuralight test demonstrated that people with Parkinson’s disease tended to have slower eye movements and look at the wrong place more often than those without the disease. These effects were more pronounced in patients with more severe disease than in those with mild to moderate disease.
The PALOMA study aimed to assess whether NeuraLight’s test could track disease progression and correlate with standard clinical assessments, including the MDS-UPDRS and Montreal Cognitive Assessment.
“These results suggest NeuraLight’s oculometric biomarkers can help measure progression in clinical trials with greater sensitivity and repeatability,” Tosin said. “In our experience, patients tolerate the short examination well, which supports repeat use over time and makes this approach well-suited for multicenter studies.”
