Accessible Parkinson’s disease detection from a gamified website: Deep learning using mouse trace data

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremor, bradykinesia, rigidity, and postural instability, which emerge only after substantial dopaminergic neuron loss. Early detection is critical for timely intervention, yet current clinical assessments and patient-reported scales are subjective and resource-intensive. To overcome these barriers, we developed a remote platform for structured mouse-tracing data collection through gamified web-based tests that require no specialized hardware. A total of 261 participants: 73 with confirmed PD, 155 non-PD, and 33 individuals with suspected PD completed three line-tracing tasks: straight line, sine wave, and spiral wave. During each task, cursor positions were recorded every 500 ms, along with screen dimensions and an in-target boolean flag. From these data, we engineered features and generated mouse trace images. We built three classes of deep learning classifiers: (1) a feed-forward neural network for engineered features; (2) fine-tuned computer vision models; and (3) multimodal models concatenating feed-forward neural network with computer vision models. Performance was evaluated under three scenarios: (i) 5-fold cross-validation on confirmed PD vs. non-PD controls; (ii) training on confirmed PD and non-PD controls, testing on suspected PD vs. non-PD controls; and (iii) training on suspected PD and non-PD controls, testing on confirmed PD vs non-PD controls. The best-performing models were image-based DenseNet-201 model with an F1 score of 0.9027 ± 0.0332 (i), multimodal ResNet-50 with an F1 score of 0.9353 ± 0.0334 (ii), and multimodal ViT with an F1 score of 0.7619 ± 0.0535 (iii). Feature importance in the best-performing models was evaluated using Gradient Shapley Additive Explanations (GradShap). Image inputs consistently proved to be most predictive. These findings suggest that models trained on confirmed PD diagnoses hold promise for early-stage PD screening.