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<p>Diagnosing Parkinson’s disease (PD) promptly, accessibly and effectively is crucial for improving patient outcomes, yet reaching this goal remains a challenge. Here we developed a diagnostic pen featuring a soft magnetoelastic tip and ferrofluid ink, capable of sensitively and quantitatively converting both on-surface and in-air writing motions into high-fidelity, analyzable signals for self-powered PD diagnostics. The diagnostic pen’s working mechanism is based on the magnetoelastic effect in its magnetoelastic tip and the dynamic movement of the ferrofluid ink. To validate the clinical potential, a pilot human study was conducted, incorporating both patients with PD and healthy participants. The diagnostic pen accurately recorded handwriting signals, and a one-dimensional convolutional neural network-assisted analysis successfully distinguished patients with PD with an average accuracy of 96.22%. Our development of the diagnostic pen represents a low-cost, widely disseminable and reliable technology with the potential to improve PD diagnostics across large populations and resource-limited areas.</p><p></p>

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Neural network-assisted personalized handwriting analysis for Parkinson’s disease diagnostics

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Guorui Chen,
  • Trinny Tat,
  • Yihao Zhou,
  • Zhaoqi Duan,
  • Junkai Zhang,
  • Kamryn Scott,
  • Xun Zhao,
  • Zeyang Liu,
  • Wei Wang,
  • Song Li,
  • Katy A. Cross,
  • Jun Chen

摘要

Diagnosing Parkinson’s disease (PD) promptly, accessibly and effectively is crucial for improving patient outcomes, yet reaching this goal remains a challenge. Here we developed a diagnostic pen featuring a soft magnetoelastic tip and ferrofluid ink, capable of sensitively and quantitatively converting both on-surface and in-air writing motions into high-fidelity, analyzable signals for self-powered PD diagnostics. The diagnostic pen’s working mechanism is based on the magnetoelastic effect in its magnetoelastic tip and the dynamic movement of the ferrofluid ink. To validate the clinical potential, a pilot human study was conducted, incorporating both patients with PD and healthy participants. The diagnostic pen accurately recorded handwriting signals, and a one-dimensional convolutional neural network-assisted analysis successfully distinguished patients with PD with an average accuracy of 96.22%. Our development of the diagnostic pen represents a low-cost, widely disseminable and reliable technology with the potential to improve PD diagnostics across large populations and resource-limited areas.