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<meta name="DC.Title" content="Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8317" />
<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: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency.

MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient’s age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve.

RESULTS: Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth.

CONCLUSIONS: To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.

AI
:   artificial intelligence
ASFNR
:   American Society of Functional Neuroradiology
AUROC
:   area under the receiver operating characteristic curve
GEE
:   generalized estimation equation
IQR
:   interquartile range
NPV
:   negative predictive value
PPV
:   positive predictive value
ROC
:   receiver operating characteristic
TBI
:   traumatic brain injury" />
<meta name="DC.Contributor" content="Bin Jiang" />
<meta name="DC.Contributor" content="Burak B. Ozkara" />
<meta name="DC.Contributor" content="Guangming Zhu" />
<meta name="DC.Contributor" content="Derek Boothroyd" />
<meta name="DC.Contributor" content="Jason W. Allen" />
<meta name="DC.Contributor" content="Daniel P. Barboriak" />
<meta name="DC.Contributor" content="Peter Chang" />
<meta name="DC.Contributor" content="Cynthia Chan" />
<meta name="DC.Contributor" content="Ruchir Chaudhari" />
<meta name="DC.Contributor" content="Hui Chen" />
<meta name="DC.Contributor" content="Anjeza Chukus" />
<meta name="DC.Contributor" content="Victoria Ding" />
<meta name="DC.Contributor" content="David Douglas" />
<meta name="DC.Contributor" content="Christopher G. Filippi" />
<meta name="DC.Contributor" content="Adam E. Flanders" />
<meta name="DC.Contributor" content="Ryan Godwin" />
<meta name="DC.Contributor" content="Syed Hashmi" />
<meta name="DC.Contributor" content="Christopher Hess" />
<meta name="DC.Contributor" content="Kevin Hsu" />
<meta name="DC.Contributor" content="Yvonne W. Lui" />
<meta name="DC.Contributor" content="Joseph A. Maldjian" />
<meta name="DC.Contributor" content="Patrik Michel" />
<meta name="DC.Contributor" content="Sahil S. Nalawade" />
<meta name="DC.Contributor" content="Vishal Patel" />
<meta name="DC.Contributor" content="Prashant Raghavan" />
<meta name="DC.Contributor" content="Haris I. Sair" />
<meta name="DC.Contributor" content="Jody Tanabe" />
<meta name="DC.Contributor" content="Kirk Welker" />
<meta name="DC.Contributor" content="Christopher T. Whitlow" />
<meta name="DC.Contributor" content="Greg Zaharchuk" />
<meta name="DC.Contributor" content="Max Wintermark" />
<meta name="article:published_time" content="2024-09-01" />
<meta name="article:section" content="Artificial Intelligence" />
<meta name="citation_title" content="Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient’s age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_publication_date" content="2024/09/01" />
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<meta name="citation_author" content="Bin Jiang" />
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<meta name="twitter:title" content="Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency.

MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient’s age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve.

RESULTS: Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth.

CONCLUSIONS: To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.

AI
:   artificial intelligence
ASFNR
:   American Society of Functional Neuroradiology
AUROC
:   area under the receiver operating characteristic curve
GEE
:   generalized estimation equation
IQR
:   interquartile range
NPV
:   negative predictive value
PPV
:   positive predictive value
ROC
:   receiver operating characteristic
TBI
:   traumatic brain injury" />
<meta name="og-title" property="og:title" content="Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/45/9/1276" />
<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: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency.

MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set. The data set encompassed studies with normal findings as well as those with pathologies, including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these, task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient’s age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed the performance of each model. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each artificial intelligence model, along with the area under the ROC curve.

RESULTS: Four teams processed 1177 studies. The median age of patients was 62 years, with an interquartile range of 33 years. Nineteen teams from various academic institutions registered for the competition. Of these, 4 teams submitted their final results. No commercial entities participated in the competition. For task 1, areas under the ROC curve ranged from 0.49 to 0.59. For task 2, two teams completed the task with area under the ROC curve values of 0.57 and 0.52. For task 3, teams had little-to-no agreement with the ground truth.

CONCLUSIONS: To assess the performance of artificial intelligence models in real-world clinical scenarios, we analyzed their performance in the ASFNR Artificial Intelligence Competition. The first ASFNR Competition underscored the gap between expectation and reality; and the models largely fell short in their assessments. As the integration of artificial intelligence tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.

AI
:   artificial intelligence
ASFNR
:   American Society of Functional Neuroradiology
AUROC
:   area under the receiver operating characteristic curve
GEE
:   generalized estimation equation
IQR
:   interquartile range
NPV
:   negative predictive value
PPV
:   positive predictive value
ROC
:   receiver operating characteristic
TBI
:   traumatic brain injury" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node110102--5" data-node-nid="110102" id="top-node-110102--620797633" data-pisa="ajnr;45/9/1276" data-pisa-master="ajnr;ajnr.A8317" data-apath="/ajnr/45/9/1276.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">Artificial Intelligence</span></span></div>
  
      <div class="highwire-cite-access"><span class="highwire-citation-access highwire-citation-access-check" data-pisa-id="ajnr;ajnr.A8317" data-atom-uri="/ajnr/45/9/1276.atom" data-request-view="full"></span></div>
  
      <h1 class="highwire-cite-title" id="page-title">Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Bin Jiang</span>, <span class="highwire-citation-author" data-delta="1">Burak B. Ozkara</span>, <span class="highwire-citation-author" data-delta="2">Guangming Zhu</span>, <span class="highwire-citation-author" data-delta="3">Derek Boothroyd</span>, <span class="highwire-citation-author" data-delta="4">Jason W. Allen</span>, <span class="highwire-citation-author" data-delta="5">Daniel P. Barboriak</span>, <span class="highwire-citation-author" data-delta="6">Peter Chang</span>, <span class="highwire-citation-author" data-delta="7">Cynthia Chan</span>, <span class="highwire-citation-author" data-delta="8">Ruchir Chaudhari</span>, <span class="highwire-citation-author" data-delta="9">Hui Chen</span>, <span class="highwire-citation-author" data-delta="10">Anjeza Chukus</span>, <span class="highwire-citation-author" data-delta="11">Victoria Ding</span>, <span class="highwire-citation-author" data-delta="12">David Douglas</span>, <span class="highwire-citation-author" data-delta="13">Christopher G. Filippi</span>, <span class="highwire-citation-author" data-delta="14">Adam E. Flanders</span>, <span class="highwire-citation-author" data-delta="15">Ryan Godwin</span>, <span class="highwire-citation-author" data-delta="16">Syed Hashmi</span>, <span class="highwire-citation-author" data-delta="17">Christopher Hess</span>, <span class="highwire-citation-author" data-delta="18">Kevin Hsu</span>, <span class="highwire-citation-author" data-delta="19">Yvonne W. Lui</span>, <span class="highwire-citation-author" data-delta="20">Joseph A. Maldjian</span>, <span class="highwire-citation-author" data-delta="21">Patrik Michel</span>, <span class="highwire-citation-author" data-delta="22">Sahil S. Nalawade</span>, <span class="highwire-citation-author" data-delta="23">Vishal Patel</span>, <span class="highwire-citation-author" data-delta="24">Prashant Raghavan</span>, <span class="highwire-citation-author" data-delta="25">Haris I. Sair</span>, <span class="highwire-citation-author" data-delta="26">Jody Tanabe</span>, <span class="highwire-citation-author" data-delta="27">Kirk Welker</span>, <span class="highwire-citation-author" data-delta="28">Christopher T. Whitlow</span>, <span class="highwire-citation-author" data-delta="29">Greg Zaharchuk</span> and <span class="highwire-citation-author" data-delta="30">Max Wintermark</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">1276-1283; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8317 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-110102--620797633" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Bin Jiang </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 (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, 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=Bin%2BJiang%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=Jiang%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%3ABin%2BJiang%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/0000-0002-8507-0459" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Bin Jiang</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Burak B. Ozkara </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neuroradiology (B.B.O., H.C., M.W.), MD Anderson Cancer Center, Houston, Texas</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=Burak%2BB.%2BOzkara%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=Ozkara%20BB&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%3ABurak%2BB.%2BOzkara%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">Guangming Zhu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Neurology (G.Zhu), The University of Arizona, Tucson, Arizona</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=Guangming%2BZhu%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=Zhu%20G&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%3AGuangming%2BZhu%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-3"><div class="author-tooltip-name">Derek Boothroyd </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Department of Medicine (D.B., V.D.), Stanford University School of Medicine, Stanford, 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=Derek%2BBoothroyd%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=Boothroyd%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%3ADerek%2BBoothroyd%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-4"><div class="author-tooltip-name">Jason W. 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class='nlm-sup'>d</span>Department of Medicine (D.B., V.D.), Stanford University School of Medicine, Stanford, 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=Victoria%2BDing%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=Ding%20V&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%3AVictoria%2BDing%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-12"><div class="author-tooltip-name">David Douglas </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 (B.J., C.C., R.C., A.C., D.D., S.H., G.Zaharchuk), Neuroradiology Division, Stanford University, Stanford, 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=David%2BDouglas%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=Douglas%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%3ADavid%2BDouglas%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for 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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-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Bin</span> <span class="nlm-surname">Jiang</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Burak B.</span> <span class="nlm-surname">Ozkara</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Guangming</span> <span class="nlm-surname">Zhu</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Derek</span> <span class="nlm-surname">Boothroyd</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Jason W.</span> <span class="nlm-surname">Allen</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Daniel P.</span> <span class="nlm-surname">Barboriak</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Peter</span> <span class="nlm-surname">Chang</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Cynthia</span> <span class="nlm-surname">Chan</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Ruchir</span> <span class="nlm-surname">Chaudhari</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Hui</span> <span class="nlm-surname">Chen</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">Anjeza</span> <span class="nlm-surname">Chukus</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">Victoria</span> <span class="nlm-surname">Ding</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">David</span> <span class="nlm-surname">Douglas</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">Christopher G.</span> <span class="nlm-surname">Filippi</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">Adam E.</span> <span class="nlm-surname">Flanders</span></span>, <span class="highwire-citation-author" data-delta="15"><span class="nlm-given-names">Ryan</span> <span class="nlm-surname">Godwin</span></span>, <span class="highwire-citation-author" data-delta="16"><span class="nlm-given-names">Syed</span> <span class="nlm-surname">Hashmi</span></span>, <span class="highwire-citation-author" data-delta="17"><span class="nlm-given-names">Christopher</span> <span class="nlm-surname">Hess</span></span>, <span class="highwire-citation-author" data-delta="18"><span class="nlm-given-names">Kevin</span> <span class="nlm-surname">Hsu</span></span>, <span class="highwire-citation-author" data-delta="19"><span class="nlm-given-names">Yvonne W.</span> <span class="nlm-surname">Lui</span></span>, <span class="highwire-citation-author" data-delta="20"><span class="nlm-given-names">Joseph A.</span> <span class="nlm-surname">Maldjian</span></span>, <span class="highwire-citation-author" data-delta="21"><span class="nlm-given-names">Patrik</span> <span class="nlm-surname">Michel</span></span>, <span class="highwire-citation-author" data-delta="22"><span class="nlm-given-names">Sahil S.</span> <span class="nlm-surname">Nalawade</span></span>, <span class="highwire-citation-author" data-delta="23"><span class="nlm-given-names">Vishal</span> <span class="nlm-surname">Patel</span></span>, <span class="highwire-citation-author" data-delta="24"><span class="nlm-given-names">Prashant</span> <span class="nlm-surname">Raghavan</span></span>, <span class="highwire-citation-author" data-delta="25"><span class="nlm-given-names">Haris I.</span> <span class="nlm-surname">Sair</span></span>, <span class="highwire-citation-author" data-delta="26"><span class="nlm-given-names">Jody</span> <span class="nlm-surname">Tanabe</span></span>, <span class="highwire-citation-author" data-delta="27"><span class="nlm-given-names">Kirk</span> <span class="nlm-surname">Welker</span></span>, <span class="highwire-citation-author" data-delta="28"><span class="nlm-given-names">Christopher T.</span> <span class="nlm-surname">Whitlow</span></span>, <span class="highwire-citation-author" data-delta="29"><span class="nlm-given-names">Greg</span> <span class="nlm-surname">Zaharchuk</span></span>, <span class="highwire-citation-author" data-delta="30"><span class="nlm-given-names">Max</span> <span class="nlm-surname">Wintermark</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">1276-1283; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8317 </span></div>
  
  
  
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      <div class="highwire-cite-title">Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition</div>  
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    	<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">1276-1283; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8317 </span></div>
  
  
  
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