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Are we ready for automated deep brain stimulation programming?

Eric R Cole1, Svjetlana Miocinovic2

  • 1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.

Parkinsonism & Related Disorders
|February 27, 2025
PubMed
Summary
This summary is machine-generated.

Automating deep brain stimulation (DBS) programming using Parkinson's disease biomarkers and algorithms can improve accessibility and treatment. Current research supports automated DBS, but clinical deployment requires overcoming adoption barriers.

Keywords:
Artificial intelligenceBiomarkersNeuromodulationOptimizationStimulation parameters

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Medical Technology

Background:

  • Deep brain stimulation (DBS) programming is a manual, time-intensive process requiring specialized clinician expertise.
  • Current manual programming methods limit DBS accessibility, delay patient treatment initiation, and hinder the adoption of advanced DBS technologies.
  • Optimizing DBS parameters is crucial for improving patient outcomes in neurological disorders like Parkinson's disease.

Purpose of the Study:

  • To review technological advancements for automating deep brain stimulation (DBS) programming.
  • To focus on Parkinson's disease biomarkers for objective outcome measurement and algorithms for automated setting optimization.
  • To define and evaluate performance criteria (effectiveness, efficiency, ease of use) for automated DBS programming systems.

Main Methods:

  • Review of current literature on technological advancements in DBS programming automation.
  • Identification and analysis of relevant Parkinson's disease biomarkers for outcome assessment.
  • Evaluation of algorithms designed for rapid and automatic optimization of DBS parameters.
  • Assessment of automated system components against defined performance criteria.

Main Results:

  • Technological advancements provide a solid foundation for developing automated DBS programming systems.
  • Parkinson's disease biomarkers offer objective measures for evaluating stimulation effectiveness.
  • Algorithms show promise for efficiently identifying optimal DBS settings.
  • Key performance criteria for automated systems include effectiveness, efficiency, and user-friendliness.

Conclusions:

  • Automated DBS programming is feasible based on current research and technological progress.
  • The primary challenge for widespread clinical adoption is integrating and deploying the optimal combination of existing techniques.
  • Overcoming the high entry barrier is essential for the broad clinical deployment and acceptance of automated DBS programming solutions.