PhD Scientific Days 2024

Poster Session H - Theoretical and Translational Medicine 1.

Predicting Sensory Outcomes with Machine Learning in Deep Brain Stimulation

Előadó neve

Dr. Miklós, Gabriella

Neptun code

RQESWO

Előadó munkahelye

Clinic for Neurosurgery and Neurointervention, Semmelweis University

Előadó telefonszáma

+36702192576

Előadó e-mail címe

gabriella.m.miklos@gmail.com

Az előadás címe

Predicting Sensory Outcomes with Machine Learning in Deep Brain Stimulation

Szerző(k) neve és munkahelye

Gabriella Miklós^1,2,3, László Halász^1,4, Bastian E. A. Sajonz⁵, Gijs van Elswijk³, Bálint Várkuti³, Volker A. Coenen⁵, Loránd Erőss¹

1: Clinic for Neurosurgery and Neurointervention, Semmelweis University
2: János Szentágothai Doctoral School of Neurosciences, Semmelweis University
3: CereGate GmbH, Munich, Germany
4: Albert-Szentgyörgyi Medical School, Doctoral School of Clinical Medicine, Clinical and Experimental Research for Reconstructive and Organ-Sparing Surgery, Universitiy of Szeged
5: Department of Stereotactic and Functional Neurosurgery, Universitätsklinikum Freiburg

Bemutatás módja

Szóbeli

Szekció

Poster Session H - Theoretical and Translational Medicine 1.

Language of the presentation

English

Preferred session

Neurosciences

Összefoglaló szövege

In clinical deep brain stimulation (DBS) applications, stimulation-induced sensations are
typically viewed as undesired side effects. However, in emerging contexts such as computer-
brain interface applications, intentionally inducing sensations may be desirable. The
optimization of stimulation parameters, whether to mitigate or induce sensations, presents
a formidable challenge due to the complexity of the parameter space.
This study seeks to streamline this process by employing a machine learning model to
predict the outcomes of DBS stimulation.
Utilizing a dataset comprising approximately 2500 stimulation response records obtained
from 18 thalamic DBS leads implanted in 10 patients across two clinical centers, we
conducted an extensive analysis. For each stimulation trial, we employed LeadDBS (2.6) to
simulate the Volume of Tissue Activation (VTA). These VTAs served as the foundational data.
Stimulation parameters and spatial VTA metrics were integrated as features for the
prediction model.
Through rigorous cross-validation, the machine learning model demonstrated an 85%
accuracy in predicting the occurrence of sensations following DBS stimulation. Additionally,
the model exhibited a 75% accuracy in predicting the specific anatomical location where
sensations would manifest.
These results suggest the potential of machine learning-based predictions to narrow down
the search space for optimal stimulation parameters. Outcome predictions could serve as
valuable guidance for clinical DBS programming or the fine-tuning of DBS based computer-
brain interfaces.
Funding: CereGate GmbH, SE250 Scholarship

University

Semmelweis University

Supervisor

Dr. Med. Habil. Loránd Erőss PhD

Publication of my abstract

I do not give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

7506

Start

15:40

End

15:43