Evidence map›Paper›PMID 31708729›Full record

ArticleFrontiers in neuroscience2019

Deep Learning-Based Deep Brain Stimulation Targeting and Clinical Applications.

Seong-Cheol Park, Joon Hyuk Cha, Seonhwa Lee, Wooyoung Jang, Chong Sik Lee, Jung Kyo Lee

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
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

14 citing papers in PubMed, 28 citations in OpenAlex.

  1. Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026
    Article
  2. Review
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  5. Article
  6. Article
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  9. Predicting Motor Responsiveness to Deep Brain Stimulation with Machine Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021
    Article
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  12. Review
  13. Review
  14. Data Augmentation for Brain-Tumor Segmentation: A Review.Frontiers in computational neuroscience · 2019
    Review
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

6 authors at 4 institutions in 1 country.

Seong-Cheol ParkDepartment of Neurosurgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, South Korea.
Joon Hyuk ChaDepartment of Neurosurgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, South Korea.
Seonhwa LeeDepartment of Neurosurgery, Seoul Metropolitan Government - Seoul National University Boramae Medical Center, Seoul, South Korea.
Wooyoung JangDepartment of Neurology, Gangneung Asan Hospital, University of Ulsan, Gangneung, South Korea.
Chong Sik LeeDepartment of Neurology, Asan Medical Center, University of Ulsan, Seoul, South Korea.
Jung Kyo LeeDepartment of Neurosurgery, Asan Medical Center, University of Ulsan, Seoul, South Korea.
Seoul National University · KRUniversity of Ulsan · KRAsan Medical Center · KRSeoul Metropolitan Government · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe purpose of the present study was to evaluate deep learning-based image-guided surgical planning for deep brain stimulation (DBS). We developed deep learning semantic segmentation-based DBS targeting and prospectively applied the method clinically.

methodsT2

resultsMean accuracies and mean intersection over the union values were high: 0.904 and 0.813, respectively, for the 62 training images, and 0.911 and 0.821, respectively, for the 558 augmented training images when 360 augmented validation images were used. The Dice coefficient converted from the intersection over the union was 0.902 when 720 training and 198 validation images were used. Semantic segmentation was adaptive to high anatomical variations in size, shape, and asymmetry. For clinical application, two patients were assessed: one with essential tremor and another with bradykinesia and gait disturbance due to Parkinson's disease. Both improved without complications after surgery, and microelectrode recordings showed subthalamic nuclei signals in the latter patient.

conclusionThe accuracy of deep learning-based semantic segmentation may surpass that of previous methods. DBS targeting and its clinical application were made possible using accurate deep learning-based semantic segmentation, which is adaptive to anatomical variations.

Indexed as

clinical applicationconvolutional neural networkdeep brain stimulationdeep learningsemantic segmentation

Identifiers

PMID31708729
PMCPMC6821714
OpenAlexW2983154041

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.