Abstract Details

Title AI-based Classification of Parkinson’s Disease Using Quantitative Oculomotor Biomarkers

Topic Movement Disorders

Presentation(s) P1 - Poster Session 1 (8:00 AM-9:00 AM)

Poster/Presentation
Number 16-003

Objective

The development of artificial intelligence (AI) models for diagnosing Parkinson’s disease (PD) using ocular motor parameters obtained from a virtual reality (VR)-based medical device.

Background

PD diagnosis currently relies largely on clinical examination, underscoring the need for objective biomarkers to enhance diagnostic accuracy, early detection and disease monitoring. Eye movement and pupil function abnormalities have emerged as promising candidates, reflecting PD-related dysfunction in ocular motor control pathways.

Design/Methods Eighty-one patients with idiopathic PD were enrolled in Zurich (Switzerland) and Exeter (UK). Each participant underwent two visits, including standardized ocular motor testing with the VR-based medical eye tracker and corresponding manual examination. Data with poor tracking quality or excessive signal loss were excluded. The present analysis included 132 PD examinations and 148 healthy control examinations from an independent dataset. Each PD patient was matched to one control of similar age and sex using a globally optimal matching algorithm. For each parameter, standardized mean differences (SMDs) and Welch’s tests were computed. A supervised machine learning model was trained to classify PD versus controls based on the most discriminative parameters. Site-specific analyses were conducted for Zurich and Exeter.

Results

Parameters showing the largest PD–control differences included saccades (accuracy, velocity, main sequence), vergence, ocular alignment, and pupil dynamics (constriction/dilation velocity and latency). The machine learning model achieved strong discrimination: Zurich—AUC 0.96 (F1: 0.92, sensitivity: 0.96, specificity: 0.89); Exeter—AUC 0.91 (F1: 0.84, sensitivity: 0.89, specificity: 0.79).

Conclusions

Preliminary findings demonstrate that quantitative ocular motor and pupillary metrics can robustly distinguish PD from healthy individuals. Next steps include expanding the patient and control cohorts, merging datasets across sites, and correlating ocular motor parameters with clinical measures to refine diagnostic performance.

Authors/Disclosures
Pia Massatsch, PhD PRESENTER	Dr. Massatsch has received personal compensation for serving as an employee of machineMD AG. Dr. Massatsch has or had stock in machineMD.
Ana L. Coito, PhD	Dr. Coito has received personal compensation for serving as an employee of machineMD.
Erika Han, MD	Ms. Han has nothing to disclose.
HASSAN Fadavi, MD, PhD	Dr. Fadavi has nothing to disclose.
Bruno Hauser	Mr. Hauser has received personal compensation for serving as an employee of machineMD AG. Mr. Hauser has stock in machineMD AG.
Valentina Stozitzky, MD	Mrs. Stozitzky has nothing to disclose.
Thomas Li, PhD	Dr. Li has nothing to disclose.
Bettina Balint, MD (University Hospital of Zurich)	Dr. Balint has received publishing royalties from a publication relating to health care.
helen dawes, PhD, PT	The institution of Prof. dawes has received research support from University of exeter. Prof. dawes has received intellectual property interests from a discovery or technology relating to health care.
Konrad P. Weber, MD (University Hospital Zurich)	Dr. Weber has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alexion. Dr. Weber has received intellectual property interests from a discovery or technology relating to health care. Dr. Weber has a non-compensated relationship as a Medical Advisor with MachineMD that is relevant to AAN interests or activities.

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