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Utilizing a tablet-based artificial intelligence system to assess movement disorders in a prospective study.

Maximilian Purk1, Michael Fujarski2, Marlon Becker1

  • 1Institute of Medical Informatics, University of Münster, Münster, Germany.

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|June 26, 2023
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Summary

This study introduces a tablet-based system for diagnosing Parkinson's Disease (PD) and other movement disorders. Integrating digital spiral drawings with symptom questionnaires significantly improves diagnostic accuracy compared to traditional methods.

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Area of Science:

  • Neurology
  • Digital Health
  • Biomarker Discovery

Background:

  • Traditional Parkinson's Disease (PD) assessment relies on paper-based spiral drawings for motor function evaluation.
  • Emerging mobile health tools and AI offer potential for detailed biomarker analysis and improved differential diagnoses in movement disorders.

Purpose of the Study:

  • To evaluate discriminatory digital biomarkers for differentiating Parkinson's Disease patients from healthy controls and other movement disorders.
  • To assess the diagnostic accuracy of a novel tablet-based system combining symptom questionnaires and spiral drawing analysis.

Main Methods:

  • A novel tablet system assessed 24 Parkinson's Disease patients, 27 healthy controls, and 26 patients with other movement disorders.
  • The system integrated the Parkinson's Disease Non-Motor Scale questionnaire with 2-handed spiral drawings captured digitally.
  • Machine Learning classifiers and SHapley Additive exPlanations (SHAP) values were used for feature importance analysis.

Main Results:

  • Diagnostic accuracy reached 94.0% for Parkinson's Disease vs. healthy controls and 89.4% for all movement disorders vs. healthy controls.
  • Accuracy for differentiating Parkinson's Disease from other movement disorders improved from 60% to 72% with tablet-based features.
  • Integrating both symptom data and tablet-based drawing features significantly enhanced diagnostic accuracies across all evaluated tasks.

Conclusions:

  • Tablet-based spiral drawing features, captured on consumer devices, provide objective disease characterization for movement disorders.
  • This digital approach significantly improves diagnostic accuracy for Parkinson's Disease compared to symptom questionnaires alone.
  • The system holds potential for objective, home-based assessments of movement disorders.