Leveraging Eye-Tracking Signals for Neurodegenerative Disease Classification with Deep Learning Models
Summary
Deep learning models accurately classify neurodegenerative diseases (NDs) like Alzheimer's and Parkinson's using eye-tracking data. Specific eye movement tasks and pupil data enhance diagnostic capabilities for neurological conditions.
Area of Science:
- Ophthalmology
- Neurology
- Artificial Intelligence
Background:
- Neurodegenerative diseases (NDs) cause subtle, detectable changes in eye movements.
- Accurate diagnosis of NDs, including Parkinson's Disease (PD) and Alzheimer's Disease (AD), is crucial for timely intervention.
- Distinguishing PD from PD-mimics (PDM) is a clinical challenge.
Purpose of the Study:
- To investigate the efficacy of deep learning models in classifying NDs using raw eye-tracking data.
- To identify which eye movement tasks are most effective for differentiating specific NDs.
- To assess the contribution of pupil size and binocular data to classification accuracy.
Main Methods:
- Collected eye-tracking data from 133 participants (51 healthy controls, 25 AD, 39 PD, 18 PDM) during smooth pursuit, text reading, and picture description tasks.
- Applied deep learning models to analyze raw eye movement data.
- Conducted ablation studies to evaluate the impact of pupil size and binocular data.
Main Results:
- The text reading task effectively distinguished healthy controls from PD and AD patients.
- The smooth pursuit task differentiated PD from PDM.
- Incorporating pupil size improved classification accuracy (especially for CTL vs. PD), and binocular data was vital for PD vs. PDM classification.
Conclusions:
- Deep learning models can accurately classify NDs using eye-tracking data, offering a potential objective evaluation tool.
- Eye movement analysis, particularly during specific tasks, shows promise for early diagnosis and differentiation of neurological disorders.
- This approach has clinical relevance for neurology and primary care settings.
