## 5 Conclusions  
We evaluated dynamic handwriting metrics alongside the traditional screening tools and achieved an AUC of 0.910. A streamlined five-feature model maintained an equivalent performance (AUC = 0.921) while cutting the administration time by roughly 35%.  
Rather than replacing the existing tests, digital handwriting features may complement them by capturing fine motor and temporal characteristics overlooked in the standard neuropsychological batteries. In particular, the response timing and pen pressure metrics correlated consistently with global cognition, suggesting that these measures tap into clinically meaningful neural processes. For clinical implementation, digital handwriting analyses require relatively minimal training and equipment. A standard tablet with a stylus and the analysis software are sufficient, making this approach feasible for routine use in neurology clinics.  
It is important to acknowledge that the uneven distribution of MS and NMOSD patients across the cognitive status groups represents a limitation. Although we adjusted for disease type and ran subgroup analyses, larger and more balanced cohorts are needed to validate these findings. The inclusion of healthy controls would also clarify which features reflect IDD-specific impairments versus general cognitive decline.  
Future work should use longitudinal cohorts to test whether handwriting metrics predict cognitive decline or treatment response, integrate medication status as a covariate, and explore continuous (regression-based) models of cognition. Linking these metrics with neuroimaging biomarkers may further reveal the neural substrates of handwriting alterations in IDD.  
If confirmed in larger samples, handwriting-based assessments could offer scalable, remote-friendly tools for cognitive monitoring in neurological care.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15116257/s1](https://www.mdpi.com/article/10.3390/app15116257/s1)]([https://www.mdpi.com/article/10.3390/app15116257/s1](https://www.mdpi.com/article/10.3390/app15116257/s1)). File SI: Complete_Feature_List.dsx; Table S1: Detailed_Demographic_Results.png.  
Conceptualization: M.L. and L.G.; Methodology: J.Y. and J.L.; Software: J.Y.; Validation: J.Y., Y.C. and Y.S.; Formal Analysis: J.Y.; Investigation: Y.C., Y.S. and S.Z.; Resources: M.L. and L.G.; Data Curation: J.Y., Y.C. and S.Z.; Writing--Original Draft Preparation: J.Y.; Writing--Review and Editing: [PERSON] and C.Y.; Visualization: J.Y.; Supervision: [PERSON] and J.L.; Project Administration: C.Y.; Funding Acquisition: M.L. and L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 62177010) and Beijing University of Posts and Telecommunications (BUPT)'s innovation and entrepreneurship support program (2025-YC-T045; 2025-YC-T046; 2025-YC-T047). The APC was funded by the authors.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Beijing Tiantan Hospital, Capital Medical University (protocol code KY2021-150-02; date of approval: 27 April 2022).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions related to patient confidentiality, as they contain information that could compromise the privacy of the research participants.  
The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.