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BackgroundHandwriting impairment is a distinctive symptom of movement disorders (MDs), such as Parkinson's disease (PD) and Parkinsonian syndrome (PDS).ObjectivesCustom-designed digital handwriting (DHW) tools were developed to strengthen the diagnosis of MDs.MethodsParticipants 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.ResultsA 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.ConclusionsCustom-designed DHW tasks can serve as effective digital markers for PD diagnosis.
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