  Discriminating between Parkinson's Disease (PD) and Progressive Supranuclear
Palsy (PSP) is difficult due to overlapping symptoms, especially early on.
Saccades (rapid conjugate eye movements between fixation points) are affected
by both diseases but conventional saccade analyses exhibit group level
differences only. We hypothesized analyzing entire saccade raw time series
waveforms would permit superior individual level discrimination between PD,
PSP, and healthy controls (HC). 13,309 saccadic eye movements from 127
participants were analyzed using a novel, calibration-free waveform analysis
and hierarchical machine learning framework. Individual saccades were
classified based on which trained model could reconstruct each waveform with
minimum error, indicating the most likely condition. A hierarchical classifier
then predicted overall status (recently diagnosed and medication-naive 'de
novo' PD, 'established' PD on antiparkinsonian medication, PSP, and healthy
controls) by combining each participant's saccade results. This approach
substantially outperformed conventional metrics, achieving high AUROCs
distinguishing de novo PD from PSP (0.92-0.97), de novo PD from HC (0.72-0.89),
and PSP from HC (0.90-0.95), while the conventional model showed limited
performance (AUROC range: 0.45-0.75). This calibration-free waveform analysis
sets a new standard for precise saccadic classification of PD, PSP, and HC,
increasing potential for clinical adoption, remote monitoring, and screening.
