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<meta name="DC.Title" content="Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study" />
<meta name="DC.Identifier" content="10.3174/ajnr.A6174" />
<meta name="DC.Date" content="2019-09-01" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics.

MATERIALS AND METHODS: Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model.

RESULTS: We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height ( P &lt; .05) but not with age or sex.

CONCLUSIONS: Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.

CPT
:   Current Procedural Terminology
ERT
:   ensemble of regression trees
ICD-9
:   International Classification of Diseases
ML
:   machine learning
MRN
:   medical record number
SVM
:   support vector machine" />
<meta name="DC.Contributor" content="B. Gaonkar" />
<meta name="DC.Contributor" content="D. Villaroman" />
<meta name="DC.Contributor" content="J. Beckett" />
<meta name="DC.Contributor" content="C. Ahn" />
<meta name="DC.Contributor" content="M. Attiah" />
<meta name="DC.Contributor" content="D. Babayan" />
<meta name="DC.Contributor" content="J.P. Villablanca" />
<meta name="DC.Contributor" content="N. Salamon" />
<meta name="DC.Contributor" content="A. Bui" />
<meta name="DC.Contributor" content="L. Macyszyn" />
<meta name="article:published_time" content="2019-09-01" />
<meta name="article:section" content="Spine" />
<meta name="citation_title" content="Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height (&lt;i&gt;P&lt;/i&gt; &amp;lt; .05) but not with age or sex.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.&lt;/p&gt;" />
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<meta name="twitter:title" content="Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics.

MATERIALS AND METHODS: Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model.

RESULTS: We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height ( P &lt; .05) but not with age or sex.

CONCLUSIONS: Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.

CPT
:   Current Procedural Terminology
ERT
:   ensemble of regression trees
ICD-9
:   International Classification of Diseases
ML
:   machine learning
MRN
:   medical record number
SVM
:   support vector machine" />
<meta name="og-title" property="og:title" content="Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/40/9/1586" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics.

MATERIALS AND METHODS: Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model.

RESULTS: We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height ( P &lt; .05) but not with age or sex.

CONCLUSIONS: Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.

CPT
:   Current Procedural Terminology
ERT
:   ensemble of regression trees
ICD-9
:   International Classification of Diseases
ML
:   machine learning
MRN
:   medical record number
SVM
:   support vector machine" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node69880--5" data-node-nid="69880" id="top-node-69880--6203771685" data-pisa="ajnr;40/9/1586" data-pisa-master="ajnr;ajnr.A6174" data-apath="/ajnr/40/9/1586.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-jcore-article-title-complete clearfix has-author-tooltip highwire-citation-highwire-article-top-a">
  
  
      <div class="highwire-cite-overline"><span  class="highwire-cite-metadata-article-type highwire-cite-metadata">Research Article</span><span class="separator-pipe"></span><span  class="highwire-cite-metadata-art-cat highwire-cite-metadata"><span class="wrapper">Spine</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">B. Gaonkar</span>, <span class="highwire-citation-author" data-delta="1">D. Villaroman</span>, <span class="highwire-citation-author" data-delta="2">J. Beckett</span>, <span class="highwire-citation-author" data-delta="3">C. Ahn</span>, <span class="highwire-citation-author" data-delta="4">M. Attiah</span>, <span class="highwire-citation-author" data-delta="5">D. Babayan</span>, <span class="highwire-citation-author" data-delta="6">J.P. Villablanca</span>, <span class="highwire-citation-author" data-delta="7">N. Salamon</span>, <span class="highwire-citation-author" data-delta="8">A. Bui</span> and <span class="highwire-citation-author" data-delta="9">L. Macyszyn</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">September 2019,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">40 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(9) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1586-1591; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A6174 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-69880--6203771685" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">B. Gaonkar </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=B.%2BGaonkar%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Gaonkar%20B&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AB.%2BGaonkar%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0001-8827-8063" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for B. Gaonkar</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">D. Villaroman </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=D.%2BVillaroman%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Villaroman%20D&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AD.%2BVillaroman%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0003-1475-1238" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for D. Villaroman</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">J. Beckett </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=J.%2BBeckett%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Beckett%20J&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AJ.%2BBeckett%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-2392-0526" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. Beckett</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">C. Ahn </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=C.%2BAhn%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Ahn%20C&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AC.%2BAhn%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0003-3505-7915" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for C. Ahn</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">M. Attiah </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=M.%2BAttiah%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Attiah%20M&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AM.%2BAttiah%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0001-8609-7883" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M. Attiah</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">D. Babayan </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=D.%2BBabayan%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Babayan%20D&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AD.%2BBabayan%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0001-5994-5656" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for D. Babayan</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">J.P. Villablanca </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Radiology (J.P.V., N.S., A.B., L.M.), University of California, Los Angeles, Los Angeles, California.</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=J.P.%2BVillablanca%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Villablanca%20J&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AJ.P.%2BVillablanca%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-7066-0341" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J.P. Villablanca</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">N. Salamon </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Radiology (J.P.V., N.S., A.B., L.M.), University of California, Los Angeles, Los Angeles, California.</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=N.%2BSalamon%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Salamon%20N&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AN.%2BSalamon%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-3520-9467" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for N. 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Bui </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Radiology (J.P.V., N.S., A.B., L.M.), University of California, Los Angeles, Los Angeles, California.</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=A.%2BBui%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Bui%20A&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AA.%2BBui%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-4702-1373" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for A. Bui</a></li></ul></div><div class="author-tooltip-9"><div class="author-tooltip-name">L. Macyszyn </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Neurosurgery (B.G., D.V., J.B., C.A., M.A., D.B., L.M.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=L.%2BMacyszyn%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Macyszyn%20L&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AL.%2BMacyszyn%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-3706-2796" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for L. Macyszyn</a></li></ul></div></div></div>  </div>

  
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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2>Abstract</h2><div id="sec-1" class="subsection"><p id="p-1"><strong>BACKGROUND AND PURPOSE:</strong> Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics.</p></div><div id="sec-2" class="subsection"><p id="p-2"><strong>MATERIALS AND METHODS:</strong> Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model.</p></div><div id="sec-3" class="subsection"><p id="p-3"><strong>RESULTS:</strong> We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height (<em>P</em> &lt; .05) but not with age or sex.</p></div><div id="sec-4" class="subsection"><p id="p-4"><strong>CONCLUSIONS:</strong> Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.</p></div></div><div class="section glossary" id="glossary-1"><h2 class="">ABBREVIATIONS:</h2><dl id="def-list-1"><dt id="def-item-1">CPT<span class="def-item-dt-sep"></span></dt><dd>Current Procedural Terminology<span class="def-item-dd-sep"></span></dd><dt id="def-item-2">ERT<span class="def-item-dt-sep"></span></dt><dd>ensemble of regression trees<span class="def-item-dd-sep"></span></dd><dt id="def-item-3">ICD-9<span class="def-item-dt-sep"></span></dt><dd>International Classification of Diseases<span class="def-item-dd-sep"></span></dd><dt id="def-item-4">ML<span class="def-item-dt-sep"></span></dt><dd>machine learning<span class="def-item-dd-sep"></span></dd><dt id="def-item-5">MRN<span class="def-item-dt-sep"></span></dt><dd>medical record number<span class="def-item-dd-sep"></span></dd><dt id="def-item-6">SVM<span class="def-item-dt-sep"></span></dt><dd>support vector machine</dd></dl></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2019 by American Journal of Neuroradiology</li></ul><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="http://ajnr-stage2.stage.highwire.org/content/40/9/1586.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></div>  </div>

  
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 40</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 9 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Sep 2019 </div></div>
      
      
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Gaonkar</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Villaroman</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Beckett</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Ahn</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Attiah</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Babayan</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">J.P.</span> <span class="nlm-surname">Villablanca</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">N.</span> <span class="nlm-surname">Salamon</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Bui</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Macyszyn</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Sep 2019, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">40 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(9) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1586-1591; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6174 </span></div>
  
  
  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Gaonkar</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Villaroman</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Beckett</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Ahn</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">M.</span> <span class="nlm-surname">Attiah</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Babayan</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">J.P.</span> <span class="nlm-surname">Villablanca</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">N.</span> <span class="nlm-surname">Salamon</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Bui</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Macyszyn</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Sep 2019, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">40 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(9) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1586-1591; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6174 </span></div>
  
  
  
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