Evidence map›Paper›PMID 40016056›Full record

ReviewParkinsonism & related disorders2025

Are we ready for automated deep brain stimulation programming?

Eric R Cole, Svjetlana Miocinovic

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Parkinsonism & related disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Eric R ColeWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.
Svjetlana MiocinovicWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA. Electronic address: svjetlana.miocinovic@emory.edu.

Funding

Optimizing Patient-Specific Deep Brain Stimulation Models Using ElectrophysiologyR01NS125143 · NINDS · EMORY UNIVERSITY · PI Svjetlana Miocinovic · 2022 to 2026
$2.4M
NINDS NIH HHS R01 NS125143
6 · The paper itself

Abstract

Deep brain stimulation (DBS) requires individualized programming of stimulation parameters, a time-consuming process performed manually by clinicians with specialized training. This process limits DBS accessibility, delays treatment, and constrains the potential for next-generation technology to improve patient outcomes. This review describes technological advancements that could automate DBS programming, focusing on Parkinson's disease biomarkers that can provide objective outcome measurement and algorithms that can quickly and automatically identify optimal DBS settings. We first define key performance criteria for an automated programming system, including effectiveness, efficiency, and ease of use, and then describe and evaluate each component with respect to these criteria. We conclude that the state of current research provides a strong foundation for developing automated DBS programming. The primary remaining obstacle lies in identifying and deploying the best combination of available techniques that will overcome the high entry barrier needed for wide-scale clinical deployment and adoption.

Indexed as

Deep Brain StimulationParkinson DiseaseAlgorithmsHumansArtificial intelligenceBiomarkersNeuromodulationOptimizationStimulation parameters

Identifiers

PMID40016056
PMCPMC12599573

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

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.