Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.
Identifieur interne : 000099 ( Main/Exploration ); précédent : 000098; suivant : 000100Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.
Auteurs : Dan Valsky [Israël] ; Kim T. Blackwell ; Idit Tamir ; Renana Eitan ; Hagai Bergman ; Zvi IsraelSource :
- Journal of neural engineering [ 1741-2552 ] ; 2020.
Abstract
Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improves motor symptoms and quality of life. Traditionally, pallidal borders have been demarcated by electrophysiological microelectrode recordings (MERs) during DBS surgery. However, detection of pallidal borders can be challenging due to the variability of the firing characteristics of neurons encountered along the trajectory. MER can also be time-consuming and therefore costly. Here we show the feasibility of real-time machine learning classification of striato-pallidal borders to assist neurosurgeons during DBS surgery.
DOI: 10.1088/1741-2552/ab53ac
PubMed: 31675740
Affiliations:
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Author to whom any correspondence should be addressed.</nlm:affiliation><country xml:lang="fr">Israël</country><wicri:regionArea>The Edmond and Lily Safra Center for Brain Research (ELSC), The Hebrew University, Jerusalem</wicri:regionArea><wicri:noRegion>Jerusalem</wicri:noRegion></affiliation></author><author><name sortKey="Blackwell, Kim T" sort="Blackwell, Kim T" uniqKey="Blackwell K" first="Kim T" last="Blackwell">Kim T. Blackwell</name></author><author><name sortKey="Tamir, Idit" sort="Tamir, Idit" uniqKey="Tamir I" first="Idit" last="Tamir">Idit Tamir</name></author><author><name sortKey="Eitan, Renana" sort="Eitan, Renana" uniqKey="Eitan R" first="Renana" last="Eitan">Renana Eitan</name></author><author><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name></author><author><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name></author></analytic><series><title level="j">Journal of neural engineering</title><idno type="eISSN">1741-2552</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improves motor symptoms and quality of life. Traditionally, pallidal borders have been demarcated by electrophysiological microelectrode recordings (MERs) during DBS surgery. However, detection of pallidal borders can be challenging due to the variability of the firing characteristics of neurons encountered along the trajectory. MER can also be time-consuming and therefore costly. Here we show the feasibility of real-time machine learning classification of striato-pallidal borders to assist neurosurgeons during DBS surgery.</div></front></TEI><affiliations><list><country><li>Israël</li></country></list><tree><noCountry><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name><name sortKey="Blackwell, Kim T" sort="Blackwell, Kim T" uniqKey="Blackwell K" first="Kim T" last="Blackwell">Kim T. Blackwell</name><name sortKey="Eitan, Renana" sort="Eitan, Renana" uniqKey="Eitan R" first="Renana" last="Eitan">Renana Eitan</name><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name><name sortKey="Tamir, Idit" sort="Tamir, Idit" uniqKey="Tamir I" first="Idit" last="Tamir">Idit Tamir</name></noCountry><country name="Israël"><noRegion><name sortKey="Valsky, Dan" sort="Valsky, Dan" uniqKey="Valsky D" first="Dan" last="Valsky">Dan Valsky</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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