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Related Experiment Video

Updated: Jun 28, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases:

Md Rahat Shahriar Zawad1, Zerin Nasrin Tumpa2, Lydia Sollis2

  • 1Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.

JMIR Formative Research
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study shows artificial intelligence models can detect Parkinson disease (PD) using mouse-tracing data from community samples, even with less specific diagnoses. This approach supports PD screening in low-resource settings where specialist diagnoses are challenging.

Area of Science:

  • Artificial Intelligence
  • Neurodegenerative Diseases
  • Biomedical Engineering

Background:

  • Parkinson disease (PD) is a global neurodegenerative disorder primarily affecting motor function.
  • Current AI models for PD detection often rely on data from well-resourced settings with confirmed diagnoses.
  • Specialist-confirmed labels for PD are frequently unfeasible in low-resource environments.

Purpose of the Study:

  • To assess the feasibility of training AI models using data from community-recruited participants with suspected PD.
  • To determine if weaker diagnostic labels, more accessible in global health settings, can yield diagnostically useful predictive signals.
  • To develop a web platform for collecting structured mouse-tracing data for PD assessment.

Main Methods:

Keywords:
Parkinson diseasedigital diagnosticsfeasibility studymouse-trace dataremote screening

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Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
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Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

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Last Updated: Jun 28, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Published on: July 24, 2019

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
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Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

  • Recruited 261 participants (73 self-reported PD, 155 non-PD, 33 suspected PD) from community organizations.
  • Collected mouse-tracing data (cursor position, screen dimensions) during straight line, sine wave, and spiral wave tasks via a web platform.
  • Engineered features and generated mouse trace images; developed classifiers including feed-forward neural networks, deep learning computer vision models, and multimodal models.

    • Main Results:

    • Multimodal Vision Transformer achieved an F1 score of 0.7619 in the primary experiment (suspected PD vs. non-PD, tested on self-reported PD vs. non-PD).
    • Multimodal ResNet-50 achieved an F1 score of 0.9353 in the secondary analysis (self-reported PD vs. non-PD, tested on suspected PD vs. non-PD).
    • Models trained on suspected PD demonstrated meaningful performance in predicting self-reported PD, indicating feasibility of using lower-specificity labels.

    Conclusions:

    • Remotely collected mouse-tracing data can support PD screening AI models, even with low diagnostic specificity.
    • AI models trained on suspected PD from community samples may identify signals transferable to predicting actual PD.
    • Future research could involve pretraining models with weaker labels and fine-tuning with stronger clinical data.