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<meta name="DC.Title" content="Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8334" />
<meta name="DC.Date" content="2024-09-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence (AI)-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an AI model for detecting steno-occlusive lesions in the intracranial arteries.

MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from 2 institutions, which served as internal ( n  = 62) and external ( n  = 76) test sets, respectively. Each study was reviewed by 5 radiologists (2 neuroradiologists and 3 radiology residents) to compare the usage and nonusage of our proposed AI model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed by using the area under the jackknife free-response receiver operating characteristic curve (AUFROC) and reading time for comparison.

RESULTS: The average AUFROC for the 5 radiologists demonstrated an improvement from 0.70 without AI to 0.76 with AI ( P  = .027). Notably, this improvement was most pronounced among the 3 radiology residents, whose performance metrics increased from 0.68 to 0.76 ( P  = .002). Despite an increased reading time by using AI, there was no significant change among the readings by radiology residents. Moreover, the use of AI resulted in improved interobserver agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752).

CONCLUSIONS: Our proposed AI model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less experienced readers may benefit the most from this model.

AI
:   artificial intelligence
AUC
:   area under the receiver operating characteristic curve
AUFROC
:   area under the jackknife free-response receiver operating characteristic curve
ICC
:   intraclass correlation coefficient
JAFROC
:   jackknife free-response receiver operating characteristic" />
<meta name="DC.Contributor" content="Hunjong Lim" />
<meta name="DC.Contributor" content="Dongjun Choi" />
<meta name="DC.Contributor" content="Leonard Sunwoo" />
<meta name="DC.Contributor" content="Jae Hyeop Jung" />
<meta name="DC.Contributor" content="Sung Hyun Baik" />
<meta name="DC.Contributor" content="Se Jin Cho" />
<meta name="DC.Contributor" content="Jinhee Jang" />
<meta name="DC.Contributor" content="Tackeun Kim" />
<meta name="DC.Contributor" content="Kyong Joon Lee" />
<meta name="article:published_time" content="2024-09-01" />
<meta name="article:section" content="Artificial Intelligence" />
<meta name="citation_title" content="Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence (AI)-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an AI model for detecting steno-occlusive lesions in the intracranial arteries.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Overall, 138 TOF-MRA images were collected from 2 institutions, which served as internal (&lt;i&gt;n&lt;/i&gt; = 62) and external (&lt;i&gt;n&lt;/i&gt; = 76) test sets, respectively. Each study was reviewed by 5 radiologists (2 neuroradiologists and 3 radiology residents) to compare the usage and nonusage of our proposed AI model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed by using the area under the jackknife free-response receiver operating characteristic curve (AUFROC) and reading time for comparison.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The average AUFROC for the 5 radiologists demonstrated an improvement from 0.70 without AI to 0.76 with AI (&lt;i&gt;P &lt;/i&gt;= .027). Notably, this improvement was most pronounced among the 3 radiology residents, whose performance metrics increased from 0.68 to 0.76 (&lt;i&gt;P &lt;/i&gt;= .002). Despite an increased reading time by using AI, there was no significant change among the readings by radiology residents. Moreover, the use of AI resulted in improved interobserver agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Our proposed AI model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less experienced readers may benefit the most from this model.&lt;/p&gt;" />
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<meta name="twitter:title" content="Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence (AI)-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an AI model for detecting steno-occlusive lesions in the intracranial arteries.

MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from 2 institutions, which served as internal ( n  = 62) and external ( n  = 76) test sets, respectively. Each study was reviewed by 5 radiologists (2 neuroradiologists and 3 radiology residents) to compare the usage and nonusage of our proposed AI model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed by using the area under the jackknife free-response receiver operating characteristic curve (AUFROC) and reading time for comparison.

RESULTS: The average AUFROC for the 5 radiologists demonstrated an improvement from 0.70 without AI to 0.76 with AI ( P  = .027). Notably, this improvement was most pronounced among the 3 radiology residents, whose performance metrics increased from 0.68 to 0.76 ( P  = .002). Despite an increased reading time by using AI, there was no significant change among the readings by radiology residents. Moreover, the use of AI resulted in improved interobserver agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752).

CONCLUSIONS: Our proposed AI model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less experienced readers may benefit the most from this model.

AI
:   artificial intelligence
AUC
:   area under the receiver operating characteristic curve
AUFROC
:   area under the jackknife free-response receiver operating characteristic curve
ICC
:   intraclass correlation coefficient
JAFROC
:   jackknife free-response receiver operating characteristic" />
<meta name="og-title" property="og:title" content="Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/9/1253" />
<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: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence (AI)-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an AI model for detecting steno-occlusive lesions in the intracranial arteries.

MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from 2 institutions, which served as internal ( n  = 62) and external ( n  = 76) test sets, respectively. Each study was reviewed by 5 radiologists (2 neuroradiologists and 3 radiology residents) to compare the usage and nonusage of our proposed AI model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed by using the area under the jackknife free-response receiver operating characteristic curve (AUFROC) and reading time for comparison.

RESULTS: The average AUFROC for the 5 radiologists demonstrated an improvement from 0.70 without AI to 0.76 with AI ( P  = .027). Notably, this improvement was most pronounced among the 3 radiology residents, whose performance metrics increased from 0.68 to 0.76 ( P  = .002). Despite an increased reading time by using AI, there was no significant change among the readings by radiology residents. Moreover, the use of AI resulted in improved interobserver agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752).

CONCLUSIONS: Our proposed AI model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less experienced readers may benefit the most from this model.

AI
:   artificial intelligence
AUC
:   area under the receiver operating characteristic curve
AUFROC
:   area under the jackknife free-response receiver operating characteristic curve
ICC
:   intraclass correlation coefficient
JAFROC
:   jackknife free-response receiver operating characteristic" />
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      <h1 class="highwire-cite-title" id="page-title">Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Hunjong Lim</span>, <span class="highwire-citation-author" data-delta="1">Dongjun Choi</span>, <span class="highwire-citation-author" data-delta="2">Leonard Sunwoo</span>, <span class="highwire-citation-author" data-delta="3">Jae Hyeop Jung</span>, <span class="highwire-citation-author" data-delta="4">Sung Hyun Baik</span>, <span class="highwire-citation-author" data-delta="5">Se Jin Cho</span>, <span class="highwire-citation-author" data-delta="6">Jinhee Jang</span>, <span class="highwire-citation-author" data-delta="7">Tackeun Kim</span> and <span class="highwire-citation-author" data-delta="8">Kyong Joon Lee</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 2024,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(9) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1253-1259; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8334 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-110108--62080334236" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Hunjong Lim </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea</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=Hunjong%2BLim%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=Lim%20H&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%3AHunjong%2BLim%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/0000-0002-8512-6977" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Hunjong Lim</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Dongjun Choi </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>lululab Inc. (D.C.), Seoul, South Korea</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=Dongjun%2BChoi%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=Choi%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 last"><a href="/search/author1%3ADongjun%2BChoi%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Leonard Sunwoo </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, South Korea</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=Leonard%2BSunwoo%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=Sunwoo%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%3ALeonard%2BSunwoo%2B" 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href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Jae%2BHyeop%2BJung%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=Jung%20JH&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%3AJae%2BHyeop%2BJung%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/0000-0002-0108-9495" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Jae Hyeop Jung</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">Sung Hyun Baik </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea</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=Sung%2BHyun%2BBaik%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=Baik%20SH&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%3ASung%2BHyun%2BBaik%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li 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(T.K.), Seoul, South Korea</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=Tackeun%2BKim%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=Kim%20T&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 last"><a href="/search/author1%3ATackeun%2BKim%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-8"><div class="author-tooltip-name">Kyong Joon Lee </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea</div><div class='author-affiliation'><span class='nlm-sup'>g</span>Monitor Corp. 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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" xmlns:xhtml="http://www.w3.org/1999/xhtml" class="section ref-list" id="ref-list-1"><h2 class="">References</h2><ol class="cit-list ref-use-labels"><li><span class="ref-label">1.</span><a class="rev-xref-ref" href="#xref-ref-1-1" title="View reference 1. in text" id="ref-1">↵</a><div class="cit ref-cit ref-journal" id="cit-45.9.1253.1" data-doi="10.1016/S1474-4422(17)30299-5"><div class="cit-metadata"><cite><span class="cit-auth cit-collab">Group GBDNDC</span>. <span class="cit-article-title">Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015</span>. <abbr class="cit-jnl-abbrev">Lancet Neurol</abbr> <span class="cit-pub-date">2017</span>;<span class="cit-vol">16</span>:<span class="cit-fpage">877</span>–<span class="cit-lpage">97</span></cite></div><div class="cit-extra"><a href="/lookup/external-ref?access_num=10.1016/S1474-4422(17)30299-5&amp;link_type=DOI" class="cit-ref-sprinkles cit-ref-sprinkles-doi cit-ref-sprinkles-crossref"><span>CrossRef</span></a><a href="/lookup/external-ref?access_num=28931491&amp;link_type=MED&amp;atom=%2Fajnr%2F45%2F9%2F1253.atom" class="cit-ref-sprinkles cit-ref-sprinkles-medline"><span>PubMed</span></a></div></div></li><li><span class="ref-label">2.</span><a class="rev-xref-ref" href="#xref-ref-2-1" title="View reference 2. in text" id="ref-2">↵</a><div class="cit ref-cit ref-journal" id="cit-45.9.1253.2" data-doi="10.1055/s-0038-1649503"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">Katan</span>  <span class="cit-name-given-names">M</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Luft</span>  <span class="cit-name-given-names">A</span></span></li></ol><cite>. <span class="cit-article-title">Global burden of stroke</span>. <abbr 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href="#xref-ref-3-1" title="View reference 3. in text" id="ref-3">↵</a><div class="cit ref-cit ref-journal" id="cit-45.9.1253.3" data-doi="10.1161/STROKEAHA.110.597278"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">Arenillas</span>  <span class="cit-name-given-names">JF</span></span></li></ol><cite>. <span class="cit-article-title">Intracranial atherosclerosis: current concepts</span>. <abbr class="cit-jnl-abbrev">Stroke</abbr> <span class="cit-pub-date">2011</span>;<span class="cit-vol">42</span>:<span class="cit-fpage">S20</span>–<span class="cit-lpage">23</span><span class="cit-pub-id-sep cit-pub-id-doi-sep"> </span><span class="cit-pub-id cit-pub-id-doi"><span class="cit-pub-id-scheme-doi">doi:</span>10.1161/STROKEAHA.110.597278</span><span class="cit-pub-id-sep cit-pub-id-pmid-sep"> </span><span class="cit-pub-id cit-pub-id-pmid"><span class="cit-pub-id-scheme-pmid">pmid:</span>21164126</span></cite></div><div class="cit-extra"><a href="/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6OToic3Ryb2tlYWhhIjtzOjU6InJlc2lkIjtzOjE2OiI0Mi8xX3N1cHBsXzEvUzIwIjtzOjQ6ImF0b20iO3M6MjA6Ii9ham5yLzQ1LzkvMTI1My5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=" class="cit-ref-sprinkles cit-ref-sprinkles-ijlink"><span><span class="cit-reflinks-abstract">Abstract</span><span class="cit-sep cit-reflinks-variant-name-sep">/</span><span class="cit-reflinks-full-text"><span class="free-full-text">FREE </span>Full Text</span></span></a></div></div></li><li><span class="ref-label">4.</span><a class="rev-xref-ref" href="#xref-ref-4-1" title="View reference 4. in text" id="ref-4">↵</a><div class="cit ref-cit ref-journal" id="cit-45.9.1253.4" data-doi="10.1161/01.STR.26.1.14"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">Sacco</span>  <span class="cit-name-given-names">RL</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Kargman</span>  <span class="cit-name-given-names">DE</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Gu</span>  <span class="cit-name-given-names">Q</span></span>, <span class="cit-etal">et al</span></li></ol><cite>. <span class="cit-article-title">Race-ethnicity and determinants of intracranial atherosclerotic cerebral infarction. The Northern Manhattan Stroke Study</span>. <abbr class="cit-jnl-abbrev">Stroke</abbr> <span class="cit-pub-date">1995</span>;<span class="cit-vol">26</span>:<span class="cit-fpage">14</span>–<span class="cit-lpage">20</span><span class="cit-pub-id-sep cit-pub-id-doi-sep"> </span><span class="cit-pub-id cit-pub-id-doi"><span class="cit-pub-id-scheme-doi">doi:</span>10.1161/01.str.26.1.14</span><span class="cit-pub-id-sep cit-pub-id-pmid-sep"> </span><span class="cit-pub-id cit-pub-id-pmid"><span class="cit-pub-id-scheme-pmid">pmid:</span>7839388</span></cite></div><div class="cit-extra"><a href="/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6OToic3Ryb2tlYWhhIjtzOjU6InJlc2lkIjtzOjc6IjI2LzEvMTQiO3M6NDoiYXRvbSI7czoyMDoiL2FqbnIvNDUvOS8xMjUzLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==" class="cit-ref-sprinkles cit-ref-sprinkles-ijlink"><span><span class="cit-reflinks-abstract">Abstract</span><span class="cit-sep cit-reflinks-variant-name-sep">/</span><span class="cit-reflinks-full-text"><span class="free-full-text">FREE </span>Full Text</span></span></a></div></div></li><li><span class="ref-label">5.</span><a class="rev-xref-ref" href="#xref-ref-5-1" title="View reference 5. in text" id="ref-5">↵</a><div class="cit ref-cit ref-journal" id="cit-45.9.1253.5" data-doi="10.1161/01.STR.27.11.1974"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">Wityk</span>  <span class="cit-name-given-names">RJ</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Lehman</span>  <span class="cit-name-given-names">D</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Klag</span>  <span class="cit-name-given-names">M</span></span>, <span class="cit-etal">et al</span></li></ol><cite>. <span class="cit-article-title">Race and sex differences 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 45</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 9 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Sep 2024 </div></div>
      
      
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      <div class="highwire-cite-title">Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Hunjong</span> <span class="nlm-surname">Lim</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Dongjun</span> <span class="nlm-surname">Choi</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Leonard</span> <span class="nlm-surname">Sunwoo</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Jae Hyeop</span> <span class="nlm-surname">Jung</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Sung Hyun</span> <span class="nlm-surname">Baik</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Se Jin</span> <span class="nlm-surname">Cho</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Jinhee</span> <span class="nlm-surname">Jang</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Tackeun</span> <span class="nlm-surname">Kim</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Kyong Joon</span> <span class="nlm-surname">Lee</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 2024, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">45 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(9) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1253-1259; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8334 </span></div>
  
  
  
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