OperaV1, Ncbi, Merge, bibRecord, 000B40

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

Identifieur interne : 000B40 ( Ncbi/Merge ); précédent : 000B39; suivant : 000B41

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

Auteurs : C. Salvatore [Italie] ; A. Cerasa [Italie] ; I. Castiglioni [Italie] ; F. Gallivanone [Italie] ; A. Augimeri [Italie] ; M. Lopez [Italie] ; G. Arabia [Italie] ; M. Morelli [Italie] ; M C Gilardi [Italie] ; A. Quattrone [Italie]

Source :

Journal of neuroscience methods ; 2014.

RBID : pubmed:24286700

English descriptors

Abstract

Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).

DOI: 10.1016/j.jneumeth.2013.11.016
PubMed: 24286700

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to stream PubMed, to step Curation: 000195
to stream PubMed, to step Checkpoint: 000155

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pubmed:24286700

Le document en format XML

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<term>Corpus Callosum (pathology)</term>
<term>Diagnosis, Differential</term>
<term>Female</term>
<term>Humans</term>
<term>Image Interpretation, Computer-Assisted</term>
<term>Magnetic Resonance Imaging</term>
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<term>Supranuclear Palsy, Progressive (pathology)</term>
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<term>Magnetic Resonance Imaging</term>
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<front><div type="abstract" xml:lang="en">Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).</div>
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<DateCreated><Year>2014</Year>
<Month>01</Month>
<Day>09</Day>
</DateCreated>
<DateCompleted><Year>2014</Year>
<Month>10</Month>
<Day>06</Day>
</DateCompleted>
<DateRevised><Year>2015</Year>
<Month>11</Month>
<Day>19</Day>
</DateRevised>
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<JournalIssue CitedMedium="Internet"><Volume>222</Volume>
<PubDate><Year>2014</Year>
<Month>Jan</Month>
<Day>30</Day>
</PubDate>
</JournalIssue>
<Title>Journal of neuroscience methods</Title>
<ISOAbbreviation>J. Neurosci. Methods</ISOAbbreviation>
</Journal>
<ArticleTitle>Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.</ArticleTitle>
<Pagination><MedlinePgn>230-7</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1016/j.jneumeth.2013.11.016</ELocationID>
<ELocationID EIdType="pii" ValidYN="Y">S0165-0270(13)00399-3</ELocationID>
<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).</AbstractText>
<AbstractText Label="METHOD" NlmCategory="METHODS">Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP.</AbstractText>
<AbstractText Label="COMPARISON WITH EXISTING METHODS" NlmCategory="METHODS">Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice.</AbstractText>
<CopyrightInformation>Copyright © 2013 Elsevier B.V. All rights reserved.</CopyrightInformation>
</Abstract>
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<ForeName>C</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>Department of Physics, University of Milan - Bicocca, Piazza della Scienza 3, 20126 Milan, Italy. Electronic address: christian.salvatore@unimib.it.</Affiliation>
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<AffiliationInfo><Affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy. Electronic address: a.cerasa@unicz.it.</Affiliation>
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<Initials>A</Initials>
<AffiliationInfo><Affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy.</Affiliation>
</AffiliationInfo>
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<Initials>M</Initials>
<AffiliationInfo><Affiliation>DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, Italy. Electronic address: miriamlp@ugr.es.</Affiliation>
</AffiliationInfo>
</Author>
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<ForeName>G</ForeName>
<Initials>G</Initials>
<AffiliationInfo><Affiliation>Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</Affiliation>
</AffiliationInfo>
</Author>
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<ForeName>M</ForeName>
<Initials>M</Initials>
<AffiliationInfo><Affiliation>Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</Affiliation>
</AffiliationInfo>
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<Initials>MC</Initials>
<AffiliationInfo><Affiliation>Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), via F.lli Cervi 93, 20090 Segrate, MI, Italy.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Quattrone</LastName>
<ForeName>A</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Neuroimaging Research Unit, Institute of Neurological Sciences, National Research Council, Germaneto, CZ, Italy; Institute of Neurology, University "Magna Graecia", Germaneto, CZ, Italy.</Affiliation>
</AffiliationInfo>
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<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType>
<PublicationType UI="D016428">Journal Article</PublicationType>
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<ArticleDate DateType="Electronic"><Year>2013</Year>
<Month>11</Month>
<Day>26</Day>
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<MedlineJournalInfo><Country>Netherlands</Country>
<MedlineTA>J Neurosci Methods</MedlineTA>
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Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

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