Automated eye-tracking for Parkinson's disease diagnosis: a proof-of-concept cascade classifier study

Parkinson's disease (PD) is a progressive neurodegenerative disorder of increasing prevalence, with diagnostic accuracy of approximately 26% in early symptomatic patients. There is a need for accurate, non-invasive biomarkers to aid in disease diagnosis.

This proof-of-concept study enrolled 90 participants (PD n = 30, other movement disorders [OM] n = 30, healthy controls [HC] n = 30) at a single institution. Participants completed two 10-min eye- tracking sessions using the SaccadeDX™ 250-Hz binocular system. A two-level cascade classifier was fitted using elastic-net feature selection followed by logistic regression on the selected features, validated by 10-fold cross-validation. The cascade distinguished HC from movement disorders (Level 1) and PD from OM (Level 2), with the objective of establishing clinical validity - that an eye-tracking signal correlates reliably with PD diagnosis.

Level 1 achieved an area under the curve (AUC) of 0.818 (95% CI: 0.71, 0.91), with a sensitivity of 83% and specificity of 63%. Level 2 achieved an AUC of 0.670 (95% CI: 0.52, 0.80), with a sensitivity of 68% and specificity of 63%. As a proof-of-concept study, the observed performance positions the device as an adjunctive screening tool. Diagnostic discrimination can only be achieved through a much larger Phase III study. Five adverse events were recorded (three cases of dizziness, one of nausea, and one of dry eyes); one participant withdrew from the study.

Preliminary evidence supports clinical validity pending confirmatory Phase III study: reproducible eye-tracking signal for PD is detectable using a two-level cascade classifier. A multi-center confirmatory study is warranted before assessment of clinical utility.Trial registration. ClinicalTrials.govNCT04925622.