ReviewBrain sciences2020
Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review.
Review in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson's disease.PloS one · 2026Article
- Revolutionizing Neurodegenerative Disease Management: The Synergy of AI and Pharmacy.Current aging science · 2026Review
- Artificial Intelligence-Driven Neuromodulation in Neurodegenerative Disease: Precision in Chaos, Learning in Loss.Biomedicines · 2025Review
- Electrical Stimulation of Cells: Drivers, Technology, and Effects.Chemical reviews · 2025Review
- Optimal control for stochastic neural oscillators.Biological cybernetics · 2025Article
- Abnormal subthalamic nucleus functional connectivity and machine learning classification in Parkinson's disease: a multisite functional magnetic resonance imaging study.Frontiers in aging neuroscience · 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
- Model-based closed-loop control of thalamic deep brain stimulation.Frontiers in network physiology · 2024Article
- A feasibility study on AI-controlled closed-loop electrical stimulation implants.Scientific reports · 2023Article
- Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review.JMIR research protocols · 2023Article
- Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence.Biomedicines · 2023Review
- M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.Sensors (Basel, Switzerland) · 2022Article
- Magnetic resonance imaging image analysis of the therapeutic effect and neuroprotective effect of deep brain stimulation in Parkinson's disease based on a deep learning algorithm.International journal for numerical methods in biomedical engineering · 2022Article
- Dynamic Prediction of Mechanical Thrombectomy Outcome for Acute Ischemic Stroke Patients Using Machine Learning.Brain sciences · 2022Article
- Surface-based morphological patterns associated with neuropsychological performance, symptom severity, and treatment response in Parkinson's disease.Annals of translational medicine · 2022Article
- Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation.Experimental neurology · 2022Review
- Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Article
- Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization.Frontiers in neuroscience · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.
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What OpenQuestion holds
Registered trials
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.