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Data availability
Data supporting the results in this study are available within the Article and its Supplementary Information. Human study data are not publicly available because they contain information that could compromise research participant privacy. Source data are provided with this paper.
Code availability
The machine leaning code in this study is available via GitHub at https://github.com/JCLABShare/PD-PEN.
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Acknowledgements
J.C. acknowledges the Vernroy Makoto Watanabe Excellence in Research Award at the UCLA Samueli School of Engineering, the Office of Naval Research Young Investigator Award (award ID N00014-24-1-2065), National Institutes of Health Grant (award ID R01 CA287326), National Science Foundation Grant (award number 2425858), the American Heart Association Innovative Project Award (award ID 23IPA1054908), the American Heart Association Transformational Project Award (award ID 23TPA1141360) and the American Heart Association’s Second Century Early Faculty Independence Award (award ID 23SCEFIA1157587). S.L. acknowledges the National Institute of Health (NS126918) and the Broad Stem Cell Research Center, the Jonsson Comprehensive Cancer Center and California NanoSystems Institute at UCLA. G.C. acknowledges the Amazon Doctoral Student Fellowship from Amazon AWS and the UCLA Science Hub for Humanity and Artificial Intelligence. G.C. also acknowledges the Predoctoral Fellowship from the American Heart Association and The VIVA Foundation (award ID 24PRE1193744). T.T. and J.C. acknowledge the Caltech/UCLA joint NIH T32 Training Grant (award ID T32EB027629). We also acknowledge the careful editing from the UCLA Writing Center for a one-on-one personalized writing consultation.
Ethics declarations
Competing interests
J.C. and G.C. are inventors on a provisional patent application (UCLA case no. 2025-283) related to the development and application of the diagnostic pen, filed by the University of California, Los Angeles. The other authors declare no competing interests.
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Nature Chemical Engineering thanks Sibo Cheng, Martin J. McKeown, Huiliang Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Magnetic properties and normal field instability of the ferrofluid ink.
a, The ferromagnetic material is composed of tiny magnetic domains with magnetic spins. b, The superparamagnetic ferrofluid ink has a quick response to the external magnetic field and minimal hysteresis. c, A transmission electron microscopy image of the single domain nanomagnets inside the ferrofluid ink. Scale bar, 40 nm. Inset is the enlarged view. Scale bar, 10 nm. d, These single domain nanomagnets have a particle diameter of 7.13 ± 1.40 nm. e-g, Magnetic hysteresis loops of the ferrofluid ink. (e) Under a low external magnetic field, the single domain nanomagnets exhibit a near-random distribution due to minimal magnetic interactions. (f) As the external magnetic field increases, the nanomagnets begin to partially align along the field direction. (g) With a further increase in the magnetic field, the single domain nanomagnets become predominantly aligned with the external magnetic field. h-j, Surface topography of the ferrofluid ink under increasing external magnetic fields, resulting from the balance between surface tension, gravity, and electromagnetic stress. (h) Initial state of the substrate containing the ferrofluid ink. (i) A high-speed camera captures the frame where the spike pattern is observed. (j) Closer proximity of the external magnetic field leads to larger and more pronounced spike patterns. Scale bars, 1.5 mm.
Extended Data Fig. 2 Block diagram showing the design of the pilot human study.
To recruit the participants, study flyers are distributed, and physician referrals are requested. Telephone screenings and information sessions ensure that participants qualify according to specific inclusion and exclusion criteria. With informed consent, participants write specific tasks using a diagnostic pen. Collected clinical data is securely stored with authorized access restricted to essential team members. Finally, a convolutional neural network analyzes the handwriting signals for PD diagnostics. Figure partially created with BioRender.com.
Extended Data Fig. 3 Personalized handwriting analysis.
a, System-level design of using the diagnostic pen for personalized handwriting analysis. b, Current signals recorded while using the diagnostic pen to draw continuous wavy lines (Task 1) for three cycles on the surface. c, Current signals recorded while using the diagnostic pen to draw spirals (Task 2) for three cycles on the surface. d, Current signals recorded while using the diagnostic pen to write letters (Task 3) for three cycles on the surface. A.U., arbitrary units. Panel a partially created with BioRender.com.
Extended Data Fig. 4 Handwriting signal analysis for in-air handwriting tasks.
a, Representative current signals from a recruited participant using the diagnostic pen to draw continuous wavy lines (Task 1) for three cycles in the air. b, Representative current signals from a recruited participant using the diagnostic pen to draw spirals (Task 2) for three cycles in the air. c, Representative current signals from a recruited participant using the diagnostic pen to write letters (Task 3) for three cycles in the air.
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Chen, G., Tat, T., Zhou, Y. et al. Neural network-assisted personalized handwriting analysis for Parkinson’s disease diagnostics. Nat Chem Eng (2025). https://doi.org/10.1038/s44286-025-00219-5
Received: 24 August 2024
Accepted: 08 April 2025
Published: 02 June 2025
DOI: https://doi.org/10.1038/s44286-025-00219-5
