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Conference Paper: A machine learning based prognostic prediction of cervical myelopathy using diffusion tensor imaging

TitleA machine learning based prognostic prediction of cervical myelopathy using diffusion tensor imaging
Authors
KeywordsCervical Spondylotic Myelopathy (CSM)
Diffusion Tensor Imaging (DTI)
Machine Learning
Prognosis
Issue Date2016
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376
Citation
The 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2016), Budapest, Hungary, 27-28 July 2016. In Conference Proceedings, 2016, p. 1-4 How to Cite?
AbstractDiffusion Tensor imaging (DTI), composing of various metrics, including fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD) and radial diffusivity (RD) has been considered as a useful clinical tool to reveal microstructure of spinal cord. Previous studies have intensively applied DTI in investigating the pathology of cervical spondylotic myelopathy (CSM), as well the symptomatic level diagnosis of CSM. However, it still remains unclear whether the DTI metric could be used in the prognosis of CSM, which is of great significance for selection of the best treatment strategy. Thus, the present study attempted to establish a prognosis model of CSM based on DTI metrics using machine learning methods. Particularly, three conventional machine learning algorithms, Naive Bayesian, Least Squares Support Vector Machine (LS-SVM), and Multi-label K-nearest Neighbour (ML-KNN) were tested on DTI data from 35 CSM patients accepting surgery treatments with post-operative outcomes followed. The results showed that prognosis of CSM with DTI metrics using LS-SVM algorithms could achieve higher prediction performance, with accuracy of 88.62%, and the learning curve of LS-SVM showed that the performance would be significantly improved if the sample size is greater than 202, indicating the potential application of the prognosis prediction of CSM from DTI metrics using machine learning algorithms. © 2016 IEEE.
DescriptionTechnical Papers - Session 4: Computational Intelligence for Medical and Bioengineering Applications
Persistent Identifierhttp://hdl.handle.net/10722/232512
ISBN

 

DC FieldValueLanguage
dc.contributor.authorJin, R-
dc.contributor.authorLuk, KDK-
dc.contributor.authorCheung, JPY-
dc.contributor.authorHu, Y-
dc.date.accessioned2016-09-20T05:30:32Z-
dc.date.available2016-09-20T05:30:32Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2016), Budapest, Hungary, 27-28 July 2016. In Conference Proceedings, 2016, p. 1-4-
dc.identifier.isbn978-146739759-9-
dc.identifier.urihttp://hdl.handle.net/10722/232512-
dc.descriptionTechnical Papers - Session 4: Computational Intelligence for Medical and Bioengineering Applications-
dc.description.abstractDiffusion Tensor imaging (DTI), composing of various metrics, including fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD) and radial diffusivity (RD) has been considered as a useful clinical tool to reveal microstructure of spinal cord. Previous studies have intensively applied DTI in investigating the pathology of cervical spondylotic myelopathy (CSM), as well the symptomatic level diagnosis of CSM. However, it still remains unclear whether the DTI metric could be used in the prognosis of CSM, which is of great significance for selection of the best treatment strategy. Thus, the present study attempted to establish a prognosis model of CSM based on DTI metrics using machine learning methods. Particularly, three conventional machine learning algorithms, Naive Bayesian, Least Squares Support Vector Machine (LS-SVM), and Multi-label K-nearest Neighbour (ML-KNN) were tested on DTI data from 35 CSM patients accepting surgery treatments with post-operative outcomes followed. The results showed that prognosis of CSM with DTI metrics using LS-SVM algorithms could achieve higher prediction performance, with accuracy of 88.62%, and the learning curve of LS-SVM showed that the performance would be significantly improved if the sample size is greater than 202, indicating the potential application of the prognosis prediction of CSM from DTI metrics using machine learning algorithms. © 2016 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376-
dc.relation.ispartofIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications Proceedings-
dc.rightsIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications Proceedings. Copyright © IEEE.-
dc.rights©2016 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 (CSM)-
dc.subjectDiffusion Tensor Imaging (DTI)-
dc.subjectMachine Learning-
dc.subjectPrognosis-
dc.titleA machine learning based prognostic prediction of cervical myelopathy using diffusion tensor imaging-
dc.typeConference_Paper-
dc.identifier.emailLuk, KDK: hrmoldk@hku.hk-
dc.identifier.emailCheung, JPY: cheungjp@hku.hk-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityLuk, KDK=rp00333-
dc.identifier.authorityCheung, JPY=rp01685-
dc.identifier.authorityHu, Y=rp00432-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/CIVEMSA.2016.7524318-
dc.identifier.scopuseid_2-s2.0-84984666950-
dc.identifier.hkuros263967-
dc.identifier.spage1-
dc.identifier.epage4-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160923-

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