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Conference Paper: Identify myelopathic cervical spinal cord using diffusion tensor image: a data-driven approach

TitleIdentify myelopathic cervical spinal cord using diffusion tensor image: a data-driven approach
Authors
KeywordsCervical spondylotic myelopathy
Diffusion tensor imaging
Level diagnosis
Machine learning
Support tensor machine (STM)
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001228
Citation
The 2015 IEEE International Conference on Digital Signal Processing (DSP 2015), Singapore, 21- 24 July 2015. In Conference Proceedings, 2015, p. 548-551 How to Cite?
AbstractDiffusion tensor image (DTI) of the cervical spinal cord has been proposed to be used to identify the myelopathic level in the cervical spinal cord. Fractional anisotropy (FA) from DTI is usually used to diagnose the level of cervical spondylotic myelopathy (CSM). However, the solely use of FA value does not consider a full information of 3D multiple indices of diffusion from DTI. This study proposed to use a classification based on machine learning to extract and determine the myelopathic cord in CSM. A classification based on support tensor machine (STM) was applied on eigenvalues extracted from DTI at compressive levels of the cervical spinal cord. This is a validation study to apply STM classification in 30 patients with CSM. The benchmark of classification was the clinical level diagnosis with consensus of senior spine surgeons. The accuracy, sensitivity and specificity of the classification were evaluated in the study. Results showed the use of STM classification provided diagnostic accuracy of 89.2%, sensitivity of 71.8% and specificity of 90.1%. Using the classification based on STM, eigenvalues of DTI can be detected by computational intelligence to provide level diagnosis of CSM, which could help the surgeons to select the most appropriate surgical plan to treat CSM. © 2015 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/232521

 

DC FieldValueLanguage
dc.contributor.authorHu, Y-
dc.contributor.authorChan, TY-
dc.contributor.authorLi, X-
dc.contributor.authorMak, KC-
dc.contributor.authorLuk, KDK-
dc.contributor.authorWang, S-
dc.date.accessioned2016-09-20T05:30:36Z-
dc.date.available2016-09-20T05:30:36Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 IEEE International Conference on Digital Signal Processing (DSP 2015), Singapore, 21- 24 July 2015. In Conference Proceedings, 2015, p. 548-551-
dc.identifier.urihttp://hdl.handle.net/10722/232521-
dc.description.abstractDiffusion tensor image (DTI) of the cervical spinal cord has been proposed to be used to identify the myelopathic level in the cervical spinal cord. Fractional anisotropy (FA) from DTI is usually used to diagnose the level of cervical spondylotic myelopathy (CSM). However, the solely use of FA value does not consider a full information of 3D multiple indices of diffusion from DTI. This study proposed to use a classification based on machine learning to extract and determine the myelopathic cord in CSM. A classification based on support tensor machine (STM) was applied on eigenvalues extracted from DTI at compressive levels of the cervical spinal cord. This is a validation study to apply STM classification in 30 patients with CSM. The benchmark of classification was the clinical level diagnosis with consensus of senior spine surgeons. The accuracy, sensitivity and specificity of the classification were evaluated in the study. Results showed the use of STM classification provided diagnostic accuracy of 89.2%, sensitivity of 71.8% and specificity of 90.1%. Using the classification based on STM, eigenvalues of DTI can be detected by computational intelligence to provide level diagnosis of CSM, which could help the surgeons to select the most appropriate surgical plan to treat CSM. © 2015 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001228-
dc.relation.ispartofIEEE International Conference on Digital Signal Processing (DSP) Proceedings-
dc.rightsIEEE International Conference on Digital Signal Processing (DSP) Proceedings. Copyright © IEEE.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectCervical spondylotic myelopathy-
dc.subjectDiffusion tensor imaging-
dc.subjectLevel diagnosis-
dc.subjectMachine learning-
dc.subjectSupport tensor machine (STM)-
dc.titleIdentify myelopathic cervical spinal cord using diffusion tensor image: a data-driven approach-
dc.typeConference_Paper-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.emailMak, KC: kincmak@hku.hk-
dc.identifier.emailLuk, KDK: hrmoldk@hku.hk-
dc.identifier.authorityHu, Y=rp00432-
dc.identifier.authorityMak, KC=rp01957-
dc.identifier.authorityLuk, KDK=rp00333-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICDSP.2015.7251933-
dc.identifier.scopuseid_2-s2.0-84961369605-
dc.identifier.hkuros263991-
dc.identifier.spage548-
dc.identifier.epage551-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160923-

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