[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121516-en":3,"doc-seo-121516-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121516,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Prediction of STN-DBS outcome in Parkinson’s disease using machine learning","Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) is an established therapy for advanced Parkinson’s disease, yet patient outcomes vary substantially. Using a dataset of 408–420 patients, machine learning models predict postoperative outcomes from preoperative clinical markers. Regression models forecast MDS-UPDRS part III scores and subscores for tremor, axial symptoms, and bradykinesia & rigidity, achieving RMSEs of 9.1, 2.6, 2.5, and 5.3. Results support accurate predictions despite clinical heterogeneity and support personalized DBS planning. Future work will incorporate additional predictors such as neuroimaging.","11 (2025) 1–9  \nContents lists available at ScienceDirect  \nDeep Brain Stimulation  \njournal [homepage: www.journals.elsevier.com/deep-brain-stimulation](homepage: www.journals.elsevier.com/deep-brain-stimulation)  \n| Prediction of STN-DBS outcome in Parkinson’s disease using machine learning |  |  |  |\n| --- | --- | --- | --- |\n| Laurens A. Biesheuvel a,b,1,* , Jesús Fuentes c,1, Rob M.A. de Biea ,\u003Cbr>Bernadette C.M. van Wijk a,d , P. Rick Schuurmane, Andreas Huschc, Jorge Goncalves c,f, Martijn Beudela\u003Cbr>a Department of Neurology, Amsterdam Neuroscience Institute, Amsterdam University Medical Center,, Amsterdam, the Netherlands b Department of Intensive Care Medicine, Amsterdam University Medical Center,, Amsterdam, the Netherlands\u003Cbr>c Luxembourg Centre for Systems Biomedicine, University of Luxembourg,, Belvaux, L-4367, Luxembourg\u003Cbr>d Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam,, Amsterdam 1081 BT, the Netherlands e Department of Neurosurgery, Amsterdam Neuroscience Institute, Amsterdam University Medical Center, Amsterdam, the Netherlands\u003Cbr>f Department of Plant Sciences, Cambridge University,, Cambridge CB2 3EA, United Kingdom |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>DBS\u003Cbr>Machine learning Ai\u003Cbr>Statistics |  | Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) is an established therapy for advanced Parkinson’s disease (PD), but outcomes vary significantly among patients. Using a dataset of 408–420 PD patients (depending on outcome), we developed machine learning models to predict outcomes ofSTN-DBS based on preoperative clinical markers. Regression models predicted scores on the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III, and subscores for Tremor, Axial symptoms, and Bradykinesia & Rigidity. The models achieved root mean square errors (RMSE) of 9.1, 2.6, 2.5, and 5.3, respectively. These results demonstrate the models’ ability to provide accurate predictions despite the heterogeneity of PD. This approach refines patient selection by forecasting postoperative outcomes and enables personalized treatment planning. Future iterations will explore additional predictors, such as neuroimaging data, to further improve model performance and support clinical decision-making in DBS therapy. This study advances the use of machine learning in predictive medicine for PD. |  |\n\nIntroduction  \nDeep brain stimulation (DBS) of the subthalamic nucleus (STN) is a prominent surgical intervention for managing the symptoms of advanced Parkinson’s disease (PD). The efficacy of this therapeutic approach is well-documented, with optimized treatment yielding notable clinical outcomes [1,2]. However, the clinical improvement varies widely among patients [3–5], with some experiencing minimal or no benefit despite careful patient selection [6]. Additionally, there are risks of surgical complications such as intracranial hemorrhage and stimulation-induced side effects such as dysarthria [7,8]. Hence, identifying patients who are more likely to have a favorable outcome after DBS is crucial.  \nKey predictors for a good outcome in PD are younger age and a higher preoperative levodopa responsiveness [9–18]. However, the  \nconsistency of these relationships across studies varies, and identifying the most informative predictors remains challenging due to incomplete datasets and statistical dependencies among variables [15,19–23]. Furthermore, there is evidence that preoperative depressive symptoms, dispositional optimism, and psychosocial factors significantly influence motor and quality of life improvements post-DBS [24–27]. This warrants the inclusion of non-motor parameters such as apathy (Starkstein Apathy Scores, SAS), quality of life (Parkinson’s Disease Questionnaire-39, PDQ39), and activities of daily living (ADL). Nevertheless, factors such as precise lead positioning and postop","cbCainzPUavFHIRN","https://ap.wps.com/l/cbCainzPUavFHIRN","pdf",2571111,1,9,"English","en",105,"# Introduction\n# Machine learning for STN-DBS outcome prediction\n# Predictors and outcome variability","[{\"question\":\"What problem does the study address in STN-DBS for Parkinson’s disease?\",\"answer\":\"It addresses large inter-patient variability in postoperative outcomes, where some patients experience minimal or no benefit despite careful selection and known risks of complications and stimulation side effects.\"},{\"question\":\"Which preoperative data are used to build the machine learning prediction models?\",\"answer\":\"The models use preoperative clinical markers as input features to predict postoperative outcomes.\"},{\"question\":\"How are model performance and predicted outcomes evaluated?\",\"answer\":\"Performance is assessed using regression predictions of MDS-UPDRS part III and relevant subscores, reported with root mean square errors (RMSE), including values of 9.1, 2.6, 2.5, and 5.3 for different targets.\"}]","Prediction of STN-DBS outcome in Parkinson’s disease using machine learning | 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