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Article: Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning

TitleClassification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning
Authors
KeywordsDiffusion tensor imaging
cervical spondylotic myelopathy
diffusion indices
feature selection
machine learning
Issue Date2018
PublisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijns/ijns.shtml
Citation
International Journal of Neural Systems, 2018, v. 28 n. 2, p. 1750036 How to Cite?
AbstractIn this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.
Persistent Identifierhttp://hdl.handle.net/10722/259423
ISSN
2017 Impact Factor: 4.58
2015 SCImago Journal Rankings: 0.909

 

DC FieldValueLanguage
dc.contributor.authorWang, S-
dc.contributor.authorHu, Y-
dc.contributor.authorShen, Y-
dc.contributor.authorLi, H-
dc.date.accessioned2018-09-03T04:07:11Z-
dc.date.available2018-09-03T04:07:11Z-
dc.date.issued2018-
dc.identifier.citationInternational Journal of Neural Systems, 2018, v. 28 n. 2, p. 1750036-
dc.identifier.issn0129-0657-
dc.identifier.urihttp://hdl.handle.net/10722/259423-
dc.description.abstractIn this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.-
dc.languageeng-
dc.publisherWorld Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijns/ijns.shtml-
dc.relation.ispartofInternational Journal of Neural Systems-
dc.subjectDiffusion tensor imaging-
dc.subjectcervical spondylotic myelopathy-
dc.subjectdiffusion indices-
dc.subjectfeature selection-
dc.subjectmachine learning-
dc.titleClassification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning-
dc.typeArticle-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityHu, Y=rp00432-
dc.identifier.doi10.1142/S0129065717500368-
dc.identifier.pmid28830310-
dc.identifier.hkuros289694-
dc.identifier.volume28-
dc.identifier.issue2-
dc.identifier.spage1750036-
dc.identifier.epage1750036-
dc.publisher.placeSingapore-

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