{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:47:47Z","timestamp":1790282867588,"version":"4.1.0"},"reference-count":60,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["movementdisorders.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Movement Disord Clin Pract"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Parkinson's disease (PD) leads to handwriting abnormalities primarily characterized by micrographia. Whether micrographia manifests early in PD, worsens throughout the disease, and lastly responds to L\u2010Dopa is still under scientific debate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>We investigated the onset, progression and L\u2010Dopa responsiveness of micrographia in PD, by applying a non\u2010invasive and cheap tool of artificial intelligence\u2010 (AI)\u2010based pen\u2010and\u2010paper handwriting analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Fifty\u2010seven PD undergoing chronic L\u2010Dopa treatment were enrolled, including 30 early\u2010stage (H&amp;Y\u2009\u2264\u20092) and 27 mid\u2010advanced stage (H&amp;Y\u2009&gt;\u20092) patients, alongside 25 age\u2010 and sex\u2010matched controls. Participants completed two standardized pen\u2010and\u2010paper handwriting tasks in an ecological scenario. Handwriting samples were examined through clinically\u2010based (ie, perceptual) and AI\u2010based (ie, automatic) procedures. Both consistent (ie, average stroke size) and progressive (ie, sequential changes in stroke size) micrographia were evaluated. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the convolutional neural network (CNN) in classifying handwriting in PD and controls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Clinically\u2010 and AI\u2010based analysis revealed a general reduction in stroke size in PD supporting the concept of parkinsonian micrographia. Compared with perceptual analysis, AI\u2010based analysis clarified that micrographia manifests early during the disease, progressively worsens and poorly responds to L\u2010Dopa. The AI models achieved high accuracy in distinguishing PD patients from controls (91%), and moderate accuracy in differentiating early from mid\u2010advanced PD (77%). Lastly, the AI model was not able to detect patients in OFF and ON states.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI\u2010based handwriting analysis is a valuable non\u2010invasive and cheap tool for detecting and quantifying micrographia in PD, for telemedicine purposes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mdc3.70208","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T10:10:09Z","timestamp":1751883009000},"page":"2187-2196","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Micrographia in Parkinson's Disease: Automatic Recognition through Artificial Intelligence"],"prefix":"10.1002","volume":"12","author":[{"given":"Francesco","family":"Asci","sequence":"first","affiliation":[{"name":"Department of Neurosciences and Sensory Organs AO San 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