Multitype hand writing as a digital marker for Parkinson's disease.

Overview

1. Department of Neurology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, No.28, Qiaozhong Road, Guangzhou 510120, China
2. Department of Nuclear Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, No.28, Qiaozhong Road, Guangzhou 510120, China

Abstract

Background: Handwriting impairment is a distinctive symptom of movement disorders (MDs), such as Parkinson's disease (PD) and Parkinsonian syndrome (PDS).

Objectives: Custom-designed digital handwriting (DHW) tools were developed to strengthen the diagnosis of MDs.

Methods: Participants with MDs from the Neurology and PET departments at the First Affiliated Hospital of Guangzhou Medical University were recruited to perform digital handwriting (DHW) tasks, which included drawing a line and a cube, and writing a sentence in Chinese (Ch), English (E), and Korean (K). AV133 PET-CT/MRI was used to detect the expression of vesicular monoamine transporter 2 (VMAT2) in the brain. An attention-based one-dimensional convolutional neural network (1D-CNN) model was developed to evaluate DHW indicators for PD diagnosis.

Results: A total of 197 participants with MDs and 160 matched controls were included, among whom 55 MD patients underwent AV133 PET-CT/MR for VMAT2 quantification. The machine learning model demonstrated that the DHW scores showed excellent discrimination between MDs and controls, with an area under the receiver operating characteristic curve (AUC) of 0.982. In subgroup analyses, the L + E + K and L + E + K + Cu task combinations effectively distinguished PD from PDS, with AUC values of 0.727 and 0.717, respectively. The diagnostic agreement between the DHW and PET-VMAT2 results reached 85.45%. PET-AV133 imaging revealed a significant 33.7% downregulation of VMAT2 expression throughout the putamen in PD patients compared with PDS patients, with dramatic reductions in the posterior putamen. DHW scores from the L + E + K + Cu task combination were significantly correlated with VMAT2 expression in the caudate nucleus (q = 0.0005), specifically within the bilateral caudate heads and bodies.

Conclusions: Custom-designed DHW tasks can serve as effective digital markers for PD diagnosis.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data availability

Data will be made available by the corresponding author upon request. All baseline imaging and cognitive data are available to the research community upon request at https://github.com/mishmoth/Multitype-hand-writing-as-a-digital-marker-for-Parkinson-s-disease- The underlying code for this study is not publicly available but may be made available to qualified researchers on reasonable request from the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 1 funder, 43 references.

Cite

This paper

Liu, H., Deng, H., Wang, L., Wang, X., & Mo, M. (2026). Multitype hand writing as a digital marker for Parkinson's disease. Clinical parkinsonism & related disorders, 15, 100480. https://doi.org/10.1016/j.prdoa.2026.100480

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