Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.
Identifieur interne : 000100 ( PubMed/Checkpoint ); précédent : 000099; suivant : 000101Real-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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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.</title><author><name sortKey="Valsky, Dan" sort="Valsky, Dan" uniqKey="Valsky D" first="Dan" last="Valsky">Dan Valsky</name><affiliation wicri:level="1"><nlm:affiliation>The Edmond and Lily Safra Center for Brain Research (ELSC), The Hebrew University, Jerusalem, Israel. 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></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:31675740</idno><idno type="pmid">31675740</idno><idno type="doi">10.1088/1741-2552/ab53ac</idno><idno type="wicri:Area/PubMed/Corpus">000379</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000379</idno><idno type="wicri:Area/PubMed/Curation">000379</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000379</idno><idno type="wicri:Area/PubMed/Checkpoint">000100</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000100</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.</title><author><name sortKey="Valsky, Dan" sort="Valsky, Dan" uniqKey="Valsky D" first="Dan" last="Valsky">Dan Valsky</name><affiliation wicri:level="1"><nlm:affiliation>The Edmond and Lily Safra Center for Brain Research (ELSC), The Hebrew University, Jerusalem, Israel. 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><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">31675740</PMID><DateRevised><Year>2020</Year><Month>01</Month><Day>08</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1741-2552</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>1</Issue><PubDate><Year>2020</Year><Month>Jan</Month><Day>06</Day></PubDate></JournalIssue><Title>Journal of neural engineering</Title><ISOAbbreviation>J Neural Eng</ISOAbbreviation></Journal><ArticleTitle>Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.</ArticleTitle><Pagination><MedlinePgn>016021</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1088/1741-2552/ab53ac</ELocationID><Abstract><AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">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.</AbstractText><AbstractText Label="APPROACH" NlmCategory="METHODS">An electrophysiological dataset from 116 trajectories of 42 patients consisting of 11 774 MER segments of background spiking activity in five classes of disease was used to train the classification algorithm. The five classes included awake Parkinson's disease patients, as well as awake and lightly anesthetized genetic and non-genetic dystonia patients. A machine learning algorithm was designed to provide prediction of the striato-pallidal borders, based on hidden Markov models (HMMs) and the L<sub>1</sub>-distance measure in normalized root mean square (NRMS) and power spectra of the MER. We tested its performance prospectively against the judgment of three electrophysiologists in the operating rooms of three hospitals using newly collected data.</AbstractText><AbstractText Label="MAIN RESULTS" NlmCategory="RESULTS">The awake and the light anesthesia dystonia classes could be merged. Using MER NRMS and spectra, the machine learning algorithm was on par with the performance of the three electrophysiologists across the striatum-GPe, GPe-GPi, and GPi-exit transitions for all disease classes.</AbstractText><AbstractText Label="SIGNIFICANCE" NlmCategory="CONCLUSIONS">Machine learning algorithms enable real-time GPi navigation systems to potentially shorten the duration of electrophysiological mapping of pallidal borders, while ensuring correct pallidal border detection.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Valsky</LastName><ForeName>Dan</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>The Edmond and Lily Safra Center for Brain Research (ELSC), The Hebrew University, Jerusalem, Israel. Author to whom any correspondence should be addressed.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Blackwell</LastName><ForeName>Kim T</ForeName><Initials>KT</Initials></Author><Author ValidYN="Y"><LastName>Tamir</LastName><ForeName>Idit</ForeName><Initials>I</Initials></Author><Author ValidYN="Y"><LastName>Eitan</LastName><ForeName>Renana</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Bergman</LastName><ForeName>Hagai</ForeName><Initials>H</Initials></Author><Author ValidYN="Y"><LastName>Israel</LastName><ForeName>Zvi</ForeName><Initials>Z</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>01</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Neural Eng</MedlineTA><NlmUniqueID>101217933</NlmUniqueID><ISSNLinking>1741-2552</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>11</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>11</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>11</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">31675740</ArticleId><ArticleId IdType="doi">10.1088/1741-2552/ab53ac</ArticleId></ArticleIdList></PubmedData></pubmed><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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