Parkinson's Disease (PD) is the second most common neurodegenerative disorder globally, and current screening methods often rely on subjective evaluations. We developed deep learning-based classification models using mouse trace data collected via a web application. 315 participants (73 PD, 179 non-PD, 63 suspected PD) completed three hand movement tasks: tracing a straight line, spiral, and sinewave. We developed three types of models: (1) engineered features models, (2) computer vision models, and (3) multimodal models. Feature importance was evaluated using Gradient Shapley Additive Explanations (GradShap). The multimodal Visual transformer (ViT) model achieved the highest performance, with F1 scores of 0.8413 +- 0.0336 (PD vs. non-PD), 0.8520 +- 0.0014 (suspected PD vs. non-PD), and 0.7034 +- 0.0017 (PD vs. suspected PD). Image data proved most influential in predicting PD outcomes. These findings suggested that models trained on confirmed PD diagnoses hold significant promise for early-stage PD screening at the population level.

The authors have declared no competing interest.

This research was, in part, funded by the National Institutes of Health (NIH) Agreement NO. 1OT2OD032581-01. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the NIH.

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IRB of University of Hawaii gave ethical approval for this work (IRB, protocol #2023-00948).

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