Accuracy of dementia diagnosis-a direct comparison between radiologists and a computerized method
Identifieur interne : 008E97 ( Main/Exploration ); précédent : 008E96; suivant : 008E98Accuracy 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, Allemagne, Australie] ; Ameet C. Patel [États-Unis] ; Catherine C. Roberts [États-Unis] ; Nick C. Fox [Royaume-Uni] ; Clifford R. Jr Jack [États-Unis] ; John Ashburner [Royaume-Uni] ; Richard S. J. Frackowiak [Royaume-Uni, France, Italie]Source :
- Brain [ 0006-8950 ] ; 2008.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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:
Affiliations:
- Allemagne, Australie, France, Italie, Royaume-Uni, États-Unis
- Angleterre, Bade-Wurtemberg, District de Fribourg-en-Brisgau, District de Karlsruhe, Grand Londres, Latium, Victoria (État), Île-de-France
- Fribourg-en-Brisgau, Heidelberg, Londres, Melbourne, Paris, Rome
- University College de Londres, Université de Melbourne
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Le document en format XML
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<author><name sortKey="Mitchell, L Anne" sort="Mitchell, L Anne" uniqKey="Mitchell L" first="L. Anne" last="Mitchell">L. Anne Mitchell</name>
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</placeName>
<orgName type="university">University College de Londres</orgName>
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<country>Allemagne</country>
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<settlement type="city">Heidelberg</settlement>
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<country>Australie</country>
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<orgName type="university">Université de Melbourne</orgName>
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<author><name sortKey="Roberts, Catherine C" sort="Roberts, Catherine C" uniqKey="Roberts C" first="Catherine C." last="Roberts">Catherine C. Roberts</name>
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<author><name sortKey="Fox, Nick C" sort="Fox, Nick C" uniqKey="Fox N" first="Nick C." last="Fox">Nick C. Fox</name>
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<settlement type="city">Londres</settlement>
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<orgName type="university">University College de Londres</orgName>
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<author><name sortKey="Jack, Clifford R Jr" sort="Jack, Clifford R Jr" uniqKey="Jack C" first="Clifford R. Jr" last="Jack">Clifford R. Jr Jack</name>
<affiliation wicri:level="1"><inist:fA14 i1="11"><s1>Department of Radiology, Mayo Clinic</s1>
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<s3>USA</s3>
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<wicri:noRegion>Rochester</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Ashburner, John" sort="Ashburner, John" uniqKey="Ashburner J" first="John" last="Ashburner">John Ashburner</name>
<affiliation wicri:level="4"><inist:fA14 i1="02"><s1>Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London</s1>
<s2>London</s2>
<s3>GBR</s3>
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<settlement type="city">Londres</settlement>
</placeName>
<orgName type="university">University College de Londres</orgName>
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</author>
<author><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>
<affiliation wicri:level="4"><inist:fA14 i1="02"><s1>Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London</s1>
<s2>London</s2>
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<settlement type="city">Londres</settlement>
</placeName>
<orgName type="university">University College de Londres</orgName>
</affiliation>
<affiliation wicri:level="3"><inist:fA14 i1="12"><s1>Département d'études cognitives, Ecole Normale Supérieure</s1>
<s2>Paris</s2>
<s3>FRA</s3>
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<country>France</country>
<placeName><region type="region">Île-de-France</region>
<region type="old region">Île-de-France</region>
<settlement type="city">Paris</settlement>
</placeName>
</affiliation>
<affiliation wicri:level="3"><inist:fA14 i1="13"><s1>Laboratory of Neuroimaging, IRCCS Santa Lucia</s1>
<s2>Roma</s2>
<s3>ITA</s3>
<sZ>14 aut.</sZ>
</inist:fA14>
<country>Italie</country>
<placeName><settlement type="city">Rome</settlement>
<region nuts="2">Latium</region>
</placeName>
</affiliation>
</author>
</analytic>
<series><title level="j" type="main">Brain</title>
<title level="j" type="abbreviated">Brain</title>
<idno type="ISSN">0006-8950</idno>
<imprint><date when="2008">2008</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><title level="j" type="main">Brain</title>
<title level="j" type="abbreviated">Brain</title>
<idno type="ISSN">0006-8950</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Accuracy</term>
<term>Comparative study</term>
<term>Dementia</term>
<term>Diagnosis</term>
<term>Nervous system diseases</term>
<term>Nuclear magnetic resonance imaging</term>
<term>Radiologist</term>
<term>Vector</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Démence</term>
<term>Pathologie du système nerveux</term>
<term>Précision</term>
<term>Diagnostic</term>
<term>Etude comparative</term>
<term>Radiologue</term>
<term>Imagerie RMN</term>
<term>Vecteur</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">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.</div>
</front>
</TEI>
<affiliations><list><country><li>Allemagne</li>
<li>Australie</li>
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<tree><country name="Allemagne"><region name="Bade-Wurtemberg"><name sortKey="Kloppel, Stefan" sort="Kloppel, Stefan" uniqKey="Kloppel S" first="Stefan" last="Klöppel">Stefan Klöppel</name>
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<name sortKey="Mitchell, L Anne" sort="Mitchell, L Anne" uniqKey="Mitchell L" first="L. Anne" last="Mitchell">L. Anne Mitchell</name>
</country>
<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="Ashburner, John" sort="Ashburner, John" uniqKey="Ashburner J" first="John" last="Ashburner">John Ashburner</name>
<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"><noRegion><name sortKey="Stonnington, Cynthia M" sort="Stonnington, Cynthia M" uniqKey="Stonnington C" first="Cynthia M." last="Stonnington">Cynthia M. Stonnington</name>
</noRegion>
<name sortKey="Chen, Frederick" sort="Chen, Frederick" uniqKey="Chen F" first="Frederick" last="Chen">Frederick Chen</name>
<name sortKey="Jack, Clifford R Jr" sort="Jack, Clifford R Jr" uniqKey="Jack C" first="Clifford R. Jr" last="Jack">Clifford R. Jr 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>
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<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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<country name="Italie"><region name="Latium"><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>
</region>
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