ArticlePeerJ2020
Machine learning prediction of motor response after deep brain stimulation in Parkinson's disease-proof of principle in a retrospective cohort.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Predictive Models for Motor Outcomes From Deep Brain Stimulation in Parkinson's Disease: A Systematic Review.The European journal of neuroscience · 2026Pooled it
- Parkinson's disease severity is encoded by non-linear oscillatory interactions across cortical-subcortical networks.Scientific reports · 2026Article
- Development and application of a multi-task fusion model using susceptibility-weighted imaging-based substantia nigra and adjacent structures for prognostic prediction of subthalamic nucleus deep brain stimulation in Parkinson's disease.Brain communications · 2026Article
- Characteristics of responders to interventions for Parkinson disease: a scoping systematic review.Neurodegenerative disease management · 2025Article
- Article
- Visualizing functional network connectivity differences using an explainable machine-learning method.Physiological measurement · 2025Article
- The Application of Machine Learning to Predict Clinical Outcomes of Deep Brain Stimulation in Parkinson's Disease: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Transforming the interrelated nature of human psychoneuroendocrine health and endocrine disrupting compounds in our planet's water: from Wilhelm Waldeyer's neuron theory to an artificial intelligence extension of the human body?Frontiers in medicine · 2025Review
- Article
- Understanding societal challenges: a NeurotechFrontiers in neuroscience · 2024Article
- Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.Cureus · 2024Review
- Predictive modeling of sensory responses in deep brain stimulation.Frontiers in neurology · 2024Article
- The Role of Levodopa Challenge in Predicting the Outcome of Subthalamic Deep Brain Stimulation.Movement disorders clinical practice · 2023Article
- Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence.Biomedicines · 2023Review
- Predicting Motor Outcome and Quality of Life After Subthalamic Deep Brain Stimulation for Parkinson's Disease: The Role of Standard Screening Measures and Wearable-Data.Journal of Parkinson's disease · 2023Article
- Preoperative Levodopa Response and Deep Brain Stimulation Effects on Motor Outcomes in Parkinson's Disease: A Systematic Review.Movement disorders clinical practice · 2022Review
- Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson's disease patients.Frontiers in neuroscience · 2022Article
- Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury.Frontiers in human neuroscience · 2022Article
- PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes.International journal of computer assisted radiology and surgery · 2021Article
- Prediction of Deep Brain Stimulation Outcome in Parkinson's Disease With Connectome Based on Hemispheric Asymmetry.Frontiers in neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDespite careful patient selection for subthalamic nucleus deep brain stimulation (STN DBS), some Parkinson's disease patients show limited improvement of motor disability. Innovative predictive analysing methods hold potential to develop a tool for clinicians that reliably predicts individual postoperative motor response, by only regarding clinical preoperative variables. The main aim of preoperative prediction would be to improve preoperative patient counselling, expectation management, and postoperative patient satisfaction.
methodsWe developed a machine learning logistic regression prediction model which generates probabilities for experiencing weak motor response one year after surgery. The model analyses preoperative variables and is trained on 89 patients using a five-fold cross-validation. Imaging and neurophysiology data are left out intentionally to ensure usability in the preoperative clinical practice. Weak responders (
resultsThe model predicts weak responders with an average area under the curve of the receiver operating characteristic of 0.79 (standard deviation: 0.08), a true positive rate of 0.80 and a false positive rate of 0.24, and a diagnostic accuracy of 78%. The reported influences of individual preoperative variables are useful for clinical interpretation of the model, but cannot been interpreted separately regardless of the other variables in the model.
conclusionThe model's diagnostic accuracy confirms the utility of machine learning based motor response prediction based on clinical preoperative variables. After reproduction and validation in a larger and prospective cohort, this prediction model holds potential to support clinicians during preoperative patient counseling.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.