Non-linear temporal scaling of surgical processes.
Identifieur interne : 003321 ( PubMed/Corpus ); précédent : 003320; suivant : 003322Non-linear temporal scaling of surgical processes.
Auteurs : Germain Forestier ; François Petitjean ; Laurent Riffaud ; Pierre JanninSource :
- Artificial intelligence in medicine [ 1873-2860 ] ; 2014.
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- MESH :
Abstract
Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest. Desires to improve patient outcomes and surgeon training, and to reduce the costs of surgery, all motivate a better understanding of surgical practices. To facilitate this, surgeons have started recording the activities that are performed during surgery. New methods have to be developed to be able to make the most of this extremely rich and complex data. The objective of this work is to enable the simultaneous comparison of a set of surgeries, in order to be able to extract high-level information about surgical practices.
DOI: 10.1016/j.artmed.2014.10.007
PubMed: 25466153
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Non-linear temporal scaling of surgical processes.</title><author><name sortKey="Forestier, Germain" sort="Forestier, Germain" uniqKey="Forestier G" first="Germain" last="Forestier">Germain Forestier</name><affiliation><nlm:affiliation>MIPS EA 2332, University of Haute-Alsace, Mulhouse, France. Electronic address: germain.forestier@uha.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Petitjean, Francois" sort="Petitjean, Francois" uniqKey="Petitjean F" first="François" last="Petitjean">François Petitjean</name><affiliation><nlm:affiliation>Faculty of Information Technology, Monash University, Melbourne, Australia. Electronic address: francois.petitjean@monash.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Riffaud, Laurent" sort="Riffaud, Laurent" uniqKey="Riffaud L" first="Laurent" last="Riffaud">Laurent Riffaud</name><affiliation><nlm:affiliation>INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France; Department of Neurosurgery, Pontchaillou University Hospital, Rennes, France. Electronic address: laurent.riffaud@chu-rennes.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Jannin, Pierre" sort="Jannin, Pierre" uniqKey="Jannin P" first="Pierre" last="Jannin">Pierre Jannin</name><affiliation><nlm:affiliation>INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France. Electronic address: pierre.jannin@univ-rennes1.fr.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2014">2014</date><idno type="RBID">pubmed:25466153</idno><idno type="pmid">25466153</idno><idno type="doi">10.1016/j.artmed.2014.10.007</idno><idno type="wicri:Area/PubMed/Corpus">003321</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">003321</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Non-linear temporal scaling of surgical processes.</title><author><name sortKey="Forestier, Germain" sort="Forestier, Germain" uniqKey="Forestier G" first="Germain" last="Forestier">Germain Forestier</name><affiliation><nlm:affiliation>MIPS EA 2332, University of Haute-Alsace, Mulhouse, France. Electronic address: germain.forestier@uha.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Petitjean, Francois" sort="Petitjean, Francois" uniqKey="Petitjean F" first="François" last="Petitjean">François Petitjean</name><affiliation><nlm:affiliation>Faculty of Information Technology, Monash University, Melbourne, Australia. Electronic address: francois.petitjean@monash.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Riffaud, Laurent" sort="Riffaud, Laurent" uniqKey="Riffaud L" first="Laurent" last="Riffaud">Laurent Riffaud</name><affiliation><nlm:affiliation>INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France; Department of Neurosurgery, Pontchaillou University Hospital, Rennes, France. Electronic address: laurent.riffaud@chu-rennes.fr.</nlm:affiliation></affiliation></author><author><name sortKey="Jannin, Pierre" sort="Jannin, Pierre" uniqKey="Jannin P" first="Pierre" last="Jannin">Pierre Jannin</name><affiliation><nlm:affiliation>INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France. Electronic address: pierre.jannin@univ-rennes1.fr.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Artificial intelligence in medicine</title><idno type="eISSN">1873-2860</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Nonlinear Dynamics</term><term>Surgical Procedures, Operative</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Nonlinear Dynamics</term><term>Surgical Procedures, Operative</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest. Desires to improve patient outcomes and surgeon training, and to reduce the costs of surgery, all motivate a better understanding of surgical practices. To facilitate this, surgeons have started recording the activities that are performed during surgery. New methods have to be developed to be able to make the most of this extremely rich and complex data. The objective of this work is to enable the simultaneous comparison of a set of surgeries, in order to be able to extract high-level information about surgical practices.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25466153</PMID><DateCreated><Year>2014</Year><Month>12</Month><Day>09</Day></DateCreated><DateCompleted><Year>2015</Year><Month>08</Month><Day>04</Day></DateCompleted><DateRevised><Year>2014</Year><Month>12</Month><Day>09</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-2860</ISSN><JournalIssue CitedMedium="Internet"><Volume>62</Volume><Issue>3</Issue><PubDate><Year>2014</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Artificial intelligence in medicine</Title><ISOAbbreviation>Artif Intell Med</ISOAbbreviation></Journal><ArticleTitle>Non-linear temporal scaling of surgical processes.</ArticleTitle><Pagination><MedlinePgn>143-52</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.artmed.2014.10.007</ELocationID><ELocationID EIdType="pii" ValidYN="Y">S0933-3657(14)00122-5</ELocationID><Abstract><AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest. Desires to improve patient outcomes and surgeon training, and to reduce the costs of surgery, all motivate a better understanding of surgical practices. To facilitate this, surgeons have started recording the activities that are performed during surgery. New methods have to be developed to be able to make the most of this extremely rich and complex data. The objective of this work is to enable the simultaneous comparison of a set of surgeries, in order to be able to extract high-level information about surgical practices.</AbstractText><AbstractText Label="MATERIALS AND METHOD" NlmCategory="METHODS">We introduce non-linear temporal scaling (NLTS): a method that finds a multiple alignment of a set of surgeries. Experiments are carried out on a set of lumbar disc neurosurgeries. We assess our method both on a highly standardised phase of the surgery (closure) and on the whole surgery.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Experiments show that NLTS makes it possible to consistently derive standards of surgical practice and to understand differences between groups of surgeries. We take the training of surgeons as the common theme for the evaluation of the results and highlight, for example, the main differences between the practices of junior and senior surgeons in the removal of a lumbar disc herniation.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">NLTS is an effective and efficient method to find a multiple alignment of a set of surgeries. NLTS realigns a set of sequences along their intrinsic timeline, which makes it possible to extract standards of surgical practices.</AbstractText><CopyrightInformation>Copyright © 2014 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Forestier</LastName><ForeName>Germain</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>MIPS EA 2332, University of Haute-Alsace, Mulhouse, France. Electronic address: germain.forestier@uha.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Petitjean</LastName><ForeName>François</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Faculty of Information Technology, Monash University, Melbourne, Australia. Electronic address: francois.petitjean@monash.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Riffaud</LastName><ForeName>Laurent</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France; Department of Neurosurgery, Pontchaillou University Hospital, Rennes, France. Electronic address: laurent.riffaud@chu-rennes.fr.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jannin</LastName><ForeName>Pierre</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>INSERM MediCIS, Unit U1099 LTSI, University of Rennes 1, Rennes, France. Electronic address: pierre.jannin@univ-rennes1.fr.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>11</Month><Day>04</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Artif Intell Med</MedlineTA><NlmUniqueID>8915031</NlmUniqueID><ISSNLinking>0933-3657</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D017711" MajorTopicYN="Y">Nonlinear Dynamics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013514" MajorTopicYN="Y">Surgical Procedures, Operative</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Dynamic time warping</Keyword><Keyword MajorTopicYN="N">Surgery</Keyword><Keyword MajorTopicYN="N">Surgical process modelling</Keyword><Keyword MajorTopicYN="N">Surgical technical skills</Keyword><Keyword MajorTopicYN="N">Temporal analysis</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year><Month>09</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2014</Year><Month>10</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>10</Month><Day>26</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>12</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>12</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>8</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25466153</ArticleId><ArticleId IdType="pii">S0933-3657(14)00122-5</ArticleId><ArticleId IdType="doi">10.1016/j.artmed.2014.10.007</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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