{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T19:39:54Z","timestamp":1773949194505,"version":"3.50.1"},"reference-count":58,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"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":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      <jats:italic>GBA1<\/jats:italic>\n                      variants are the major genetic risk factor for Parkinson's Disease (PD) and account for 5\u201330% of PD cases depending on the population and age at onset of the disease.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>\n                      The aim of this study was to assess whether Artificial Intelligence (AI) could predict\n                      <jats:italic>GBA1<\/jats:italic>\n                      \u2010mutated genotype in PD (\n                      <jats:italic>GBA1<\/jats:italic>\n                      \u2010PD). Particularly, the main objective was to identify a Machine Learning (ML) model capable of accurately providing a pre\u2010test estimate of\n                      <jats:italic>GBA1<\/jats:italic>\n                      \u2010mutated status, relying on the clinical and demographic variables with the highest predictive value.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A cohort of\n                      <jats:italic>GBA1<\/jats:italic>\n                      \u2010PD patients has been paired with non\u2010mutated PD (NM\u2010PD). The dataset comprised 58\n                      <jats:italic>GBA1<\/jats:italic>\n                      \u2010PD and 58 NM\u2010PD, for each of whom 124 features were recorded. A Leave\u2010One\u2010Out cross\u2010validation method was employed for testing and SHapley Additive exPlanations (SHAP) for examine each feature's contribution. XGBoost resulted the most effective ML model for this supervised classification task.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Through AI, we developed a model based on four specific clinical features with significant impact in predicting\n                      <jats:italic>GBA1<\/jats:italic>\n                      \u2010mutated genotype with an accuracy of 73%, reaching 94% in a subset of patients where the model has a SHAP confidence level greater than 80%. These variables included family history and scores for cognitive (MDS\u2010UPDRS 1.1) and motor impairment (MDS\u2010UPDRS 3.8a and 3.8b and rigidity subscore).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study underlies the potential of AI in enhancing targeted genetic screening in PD, especially in clinical settings where resources are limited. Main limitations of this study are the modest sample size and lack of external validation. Further studies on larger, independent cohorts are needed to refine the predictive model.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mdc3.70334","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T06:51:20Z","timestamp":1757400680000},"page":"482-493","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence Predicts\n                    <scp>\n                      <i>GBA1<\/i>\n                    <\/scp>\n                    Mutated Status in Parkinson's Disease Patients"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7159-9311","authenticated-orcid":false,"given":"Giulia","family":"Di Rauso","sequence":"first","affiliation":[{"name":"Neurology Unit, Neuromotor and Rehabilitation Department Azienda USL\u2010IRCCS di Reggio Emilia  Reggio Emilia Italy"},{"name":"Clinical and Experimental Medicine PhD Program, 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