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Accuracy of dementia diagnosis-a direct comparison between radiologists and a computerized method

Identifieur interne : 008E97 ( Main/Exploration ); précédent : 008E96; suivant : 008E98

Accuracy 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 :

RBID : Pascal:08-0521915

Descripteurs français

English descriptors

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.

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Le document en format XML

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<settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
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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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<country>Allemagne</country>
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<region type="land" nuts="1">Bade-Wurtemberg</region>
<region type="district" nuts="2">District de Karlsruhe</region>
<settlement type="city">Heidelberg</settlement>
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<country>Australie</country>
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<region type="état">Victoria (État)</region>
<settlement type="city">Melbourne</settlement>
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<country>États-Unis</country>
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<country>Royaume-Uni</country>
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<settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
<region type="région" nuts="1">Grand Londres</region>
<settlement type="city">Londres</settlement>
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<orgName type="university">University College de Londres</orgName>
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<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>
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<s1>Department of Radiology, Mayo Clinic</s1>
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<country>États-Unis</country>
<wicri:noRegion>Rochester</wicri:noRegion>
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</author>
<author>
<name sortKey="Ashburner, John" sort="Ashburner, John" uniqKey="Ashburner J" first="John" last="Ashburner">John Ashburner</name>
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<country>Royaume-Uni</country>
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<settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
<region type="région" nuts="1">Grand Londres</region>
<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>
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<country>Royaume-Uni</country>
<placeName>
<settlement type="city">Londres</settlement>
<region type="country">Angleterre</region>
<region type="région" nuts="1">Grand Londres</region>
<settlement type="city">Londres</settlement>
</placeName>
<orgName type="university">University College de Londres</orgName>
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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>
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<s3>ITA</s3>
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</inist:fA14>
<country>Italie</country>
<placeName>
<settlement type="city">Rome</settlement>
<region nuts="2">Latium</region>
</placeName>
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</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>
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<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>
<li>France</li>
<li>Italie</li>
<li>Royaume-Uni</li>
<li>États-Unis</li>
</country>
<region>
<li>Angleterre</li>
<li>Bade-Wurtemberg</li>
<li>District de Fribourg-en-Brisgau</li>
<li>District de Karlsruhe</li>
<li>Grand Londres</li>
<li>Latium</li>
<li>Victoria (État)</li>
<li>Île-de-France</li>
</region>
<settlement>
<li>Fribourg-en-Brisgau</li>
<li>Heidelberg</li>
<li>Londres</li>
<li>Melbourne</li>
<li>Paris</li>
<li>Rome</li>
</settlement>
<orgName>
<li>University College de Londres</li>
<li>Université de Melbourne</li>
</orgName>
</list>
<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>
</region>
<name sortKey="Mader, Irina" sort="Mader, Irina" uniqKey="Mader I" first="Irina" last="Mader">Irina Mader</name>
<name sortKey="Mader, Irina" sort="Mader, Irina" uniqKey="Mader I" first="Irina" last="Mader">Irina Mader</name>
<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>
</region>
<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>
</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>
</region>
</country>
<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>
</country>
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</record>

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