iPad Eye Tracking Reproduces Clinical Grade Oculomotor Differences in Parkinson's Disease
Jamie Koerner1, Erin Zou2, Jessica A Karl3
1Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.
NPJ Digital Medicine
|May 19, 2026
Summary
iPad eye tracking offers a low-cost method for Parkinson
Area of Science:
- Ophthalmology
- Neurology
- Biomedical Engineering
Background:
- Parkinson's disease (PD) diagnosis and monitoring often rely on subjective clinical assessments.
- Objective, low-cost, and portable methods for assessing oculomotor function in PD are needed.
- Previous work validated an iPad-based eye-tracking system against clinical-grade equipment.
Purpose of the Study:
- To evaluate the efficacy of an iPad-based eye-tracking system for screening and monitoring Parkinson's disease.
- To compare the performance of the iPad system with a gold-standard eye tracker (EyeLink 1000 Plus) in patients with PD and healthy controls.
- To develop a classifier for subject-level PD detection using iPad-based oculomotor metrics.
Main Methods:
- Simultaneous recording of eye movements using an iPad system and EyeLink 1000 Plus in 19 healthy controls (HC) and 12 PD patients.
- Eye movement tasks included pro-saccade, anti-saccade (AS), memory-guided saccade (MGS), and self-generated saccade tasks.
- Statistical analysis involved comparing PD-HC differences across devices and developing a three-metric classifier.
Main Results:
- Concordant statistically significant differences between PD and HC groups were observed for pre-specified metrics across both devices.
- iPad-based measurements preserved clinically meaningful group-level effects, with small interaction effects between group and device.
- A three-metric iPad classifier (AS directional error rate, AS gain, MGS gain) achieved high accuracy (0.96) for subject-level PD discrimination (AUC=0.98, sensitivity=0.91, specificity=1.00).
Conclusions:
- iPad-based eye tracking provides objective, low-cost, and portable assessment of oculomotor function relevant to Parkinson's disease.
- The system demonstrates strong potential for scalable screening and longitudinal monitoring of PD.
- Tablet-based oculomotor assessment can effectively discriminate between individuals with and without Parkinson's disease at the subject level.
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