Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method
Identifieur interne : 002A52 ( Pmc/Checkpoint ); précédent : 002A51; suivant : 002A53Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method
Auteurs : Stefan Klöppel [Allemagne, Royaume-Uni] ; Cynthia M. Stonnington [Royaume-Uni, États-Unis] ; Josephine Barnes [Royaume-Uni] ; Frederick Chen [États-Unis] ; Carlton Chu [Royaume-Uni] ; Catriona D. Good [Royaume-Uni] ; Irina Mader [Allemagne] ; L. Anne Mitchell [Royaume-Uni, Australie] ; Ameet C. Patel [États-Unis] ; Catherine C. Roberts [États-Unis] ; Nick C. Fox [Royaume-Uni] ; Clifford R. Jack [États-Unis] ; John Ashburner [Royaume-Uni] ; Richard S. J. Frackowiak [Royaume-Uni, France, Italie]Source :
- Brain [ 0006-8950 ] ; 2008.
Abstract
There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.
Url:
DOI: 10.1093/brain/awn239
PubMed: 18835868
PubMed Central: 2577804
Affiliations:
- Allemagne, Australie, France, Italie, Royaume-Uni, États-Unis
- Angleterre, Arizona, Bade-Wurtemberg, District de Fribourg-en-Brisgau, Grand Londres, Latium, Victoria (État), Île-de-France
- Fribourg-en-Brisgau, Londres, Melbourne, Paris, Rome
- University College de Londres, Université de Melbourne
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<author><name sortKey="Ashburner, John" sort="Ashburner, John" uniqKey="Ashburner J" first="John" last="Ashburner">John Ashburner</name>
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<series><title level="j">Brain</title>
<idno type="ISSN">0006-8950</idno>
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<front><div type="abstract" xml:lang="en"><p>There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.</p>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">Brain</journal-id>
<journal-id journal-id-type="publisher-id">brainj</journal-id>
<journal-id journal-id-type="hwp">brain</journal-id>
<journal-title>Brain</journal-title>
<issn pub-type="ppub">0006-8950</issn>
<issn pub-type="epub">1460-2156</issn>
<publisher><publisher-name>Oxford University Press</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">18835868</article-id>
<article-id pub-id-type="pmc">2577804</article-id>
<article-id pub-id-type="doi">10.1093/brain/awn239</article-id>
<article-id pub-id-type="publisher-id">awn239</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Original Articles</subject>
</subj-group>
</article-categories>
<title-group><article-title>Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Klöppel</surname>
<given-names>Stefan</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="AFF1"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Stonnington</surname>
<given-names>Cynthia M.</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="AFF1"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Barnes</surname>
<given-names>Josephine</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chen</surname>
<given-names>Frederick</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chu</surname>
<given-names>Carlton</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Good</surname>
<given-names>Catriona D.</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Mader</surname>
<given-names>Irina</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>7</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Mitchell</surname>
<given-names>L. Anne</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>4</sup>
</xref>
<xref ref-type="aff" rid="AFF1"><sup>8</sup>
</xref>
<xref ref-type="aff" rid="AFF1"><sup>9</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Patel</surname>
<given-names>Ameet C.</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Roberts</surname>
<given-names>Catherine C.</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Fox</surname>
<given-names>Nick C.</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Jack</surname>
<given-names>Clifford R.</given-names>
<suffix>Jr</suffix>
</name>
<xref ref-type="aff" rid="AFF1"><sup>10</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ashburner</surname>
<given-names>John</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Frackowiak</surname>
<given-names>Richard S. J.</given-names>
</name>
<xref ref-type="aff" rid="AFF1"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="AFF1"><sup>11</sup>
</xref>
<xref ref-type="aff" rid="AFF1"><sup>12</sup>
</xref>
</contrib>
</contrib-group>
<aff id="AFF1"><sup>1</sup>
Department of Psychiatry and Psychotherapy and Freiburg Brain Imaging, University Clinic Freiburg, Freiburg, Germany,<sup>2</sup>
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK,<sup>3</sup>
Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, AZ, USA,<sup>4</sup>
Dementia Research Centre, University College London, Institute of Neurology, London, UK,<sup>5</sup>
Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA,<sup>6</sup>
Department of Neuroradiology, Hurstwood Park Neurosciences Centre, Brighton & Sussex Universities Hospital NHS Trust, Haywards Heath, West Sussex, UK,<sup>7</sup>
Department of Neuroradiology and Department of Neurology, Freiburg Brain Imaging, Neurocenter, University Clinic Freiburg, Freiburg, Germany,<sup>8</sup>
Department of Radiology, Austin Health, Heidelberg,<sup>9</sup>
Department of Radiology, University of Melbourne, Melbourne, Australia,<sup>10</sup>
Department of Radiology, Mayo Clinic, Rochester, USA,<sup>11</sup>
Département d’études cognitives, Ecole Normale Supérieure, Paris, France and<sup>12</sup>
Laboratory of Neuroimaging, IRCCS Santa Lucia, Roma, Italy</aff>
<author-notes><corresp>Correspondence to: Stefan Klöppel, MD, Department of Psychiatry and Psychotherapy, University Clinic Freiburg, Freiburg, Germany E-mail: <email>stefan.kloeppel@uniklinik-freiburg.de</email>
</corresp>
</author-notes>
<pub-date pub-type="ppub"><month>11</month>
<year>2008</year>
</pub-date>
<pub-date pub-type="epub"><day>3</day>
<month>10</month>
<year>2008</year>
</pub-date>
<volume>131</volume>
<issue>11</issue>
<fpage>2969</fpage>
<lpage>2974</lpage>
<history><date date-type="received"><day>15</day>
<month>4</month>
<year>2008</year>
</date>
<date date-type="rev-recd"><day>3</day>
<month>8</month>
<year>2008</year>
</date>
<date date-type="accepted"><day>3</day>
<month>9</month>
<year>2008</year>
</date>
</history>
<permissions><copyright-statement>© 2008 The Author(s)</copyright-statement>
<copyright-year>2008</copyright-year>
<license license-type="creative-commons"><p><pmc-comment>CREATIVE COMMONS</pmc-comment>
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/2.0/uk/">http://creativecommons.org/licenses/by-nc/2.0/uk/</ext-link>
) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.</p>
</license>
</permissions>
<abstract><p>There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.</p>
</abstract>
<kwd-group><kwd>MRI</kwd>
<kwd>diagnosis</kwd>
<kwd>dementia</kwd>
<kwd>support vector machine</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
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<li>France</li>
<li>Italie</li>
<li>Royaume-Uni</li>
<li>États-Unis</li>
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<li>Bade-Wurtemberg</li>
<li>District de Fribourg-en-Brisgau</li>
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<li>Latium</li>
<li>Victoria (État)</li>
<li>Île-de-France</li>
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<settlement><li>Fribourg-en-Brisgau</li>
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<country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Kloppel, Stefan" sort="Kloppel, Stefan" uniqKey="Kloppel S" first="Stefan" last="Klöppel">Stefan Klöppel</name>
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<name sortKey="Barnes, Josephine" sort="Barnes, Josephine" uniqKey="Barnes J" first="Josephine" last="Barnes">Josephine Barnes</name>
<name sortKey="Chu, Carlton" sort="Chu, Carlton" uniqKey="Chu C" first="Carlton" last="Chu">Carlton Chu</name>
<name sortKey="Fox, Nick C" sort="Fox, Nick C" uniqKey="Fox N" first="Nick C." last="Fox">Nick C. Fox</name>
<name sortKey="Frackowiak, Richard S J" sort="Frackowiak, Richard S J" uniqKey="Frackowiak R" first="Richard S. J." last="Frackowiak">Richard S. J. Frackowiak</name>
<name sortKey="Good, Catriona D" sort="Good, Catriona D" uniqKey="Good C" first="Catriona D." last="Good">Catriona D. Good</name>
<name sortKey="Mitchell, L Anne" sort="Mitchell, L Anne" uniqKey="Mitchell L" first="L. Anne" last="Mitchell">L. Anne Mitchell</name>
<name sortKey="Stonnington, Cynthia M" sort="Stonnington, Cynthia M" uniqKey="Stonnington C" first="Cynthia M." last="Stonnington">Cynthia M. Stonnington</name>
</country>
<country name="États-Unis"><region name="Arizona"><name sortKey="Stonnington, Cynthia M" sort="Stonnington, Cynthia M" uniqKey="Stonnington C" first="Cynthia M." last="Stonnington">Cynthia M. Stonnington</name>
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<name sortKey="Jack, Clifford R" sort="Jack, Clifford R" uniqKey="Jack C" first="Clifford R." last="Jack">Clifford R. Jack</name>
<name sortKey="Patel, Ameet C" sort="Patel, Ameet C" uniqKey="Patel A" first="Ameet C." last="Patel">Ameet C. Patel</name>
<name sortKey="Roberts, Catherine C" sort="Roberts, Catherine C" uniqKey="Roberts C" first="Catherine C." last="Roberts">Catherine C. Roberts</name>
</country>
<country name="Australie"><region name="Victoria (État)"><name sortKey="Mitchell, L Anne" sort="Mitchell, L Anne" uniqKey="Mitchell L" first="L. Anne" last="Mitchell">L. Anne Mitchell</name>
</region>
</country>
<country name="France"><region name="Île-de-France"><name sortKey="Frackowiak, Richard S J" sort="Frackowiak, Richard S J" uniqKey="Frackowiak R" first="Richard S. J." last="Frackowiak">Richard S. J. Frackowiak</name>
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