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- Publisher Website: 10.1142/S0129065717500368
- Scopus: eid_2-s2.0-85028324092
- PMID: 28830310
- WOS: WOS:000423207800003
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Article: Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning
Title | Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning |
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Authors | |
Keywords | Diffusion tensor imaging cervical spondylotic myelopathy diffusion indices feature selection machine learning |
Issue Date | 2018 |
Publisher | World 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? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/259423 |
ISSN | 2023 Impact Factor: 6.6 2023 SCImago Journal Rankings: 1.672 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, S | - |
dc.contributor.author | Hu, Y | - |
dc.contributor.author | Shen, Y | - |
dc.contributor.author | Li, H | - |
dc.date.accessioned | 2018-09-03T04:07:11Z | - |
dc.date.available | 2018-09-03T04:07:11Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | International Journal of Neural Systems, 2018, v. 28 n. 2, p. 1750036 | - |
dc.identifier.issn | 0129-0657 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259423 | - |
dc.description.abstract | In 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.language | eng | - |
dc.publisher | World Scientific Publishing Co Pte Ltd. The Journal's web site is located at http://www.worldscinet.com/ijns/ijns.shtml | - |
dc.relation.ispartof | International Journal of Neural Systems | - |
dc.rights | Electronic version of an article published as International Journal of Neural Systems, 2018, v. 28 n. 2, p. 1750036 [DOI:10.1142/S0129065717500368] © World Scientific Publishing Company [http://www.worldscinet.com/ijns/ijns.shtml] | - |
dc.subject | Diffusion tensor imaging | - |
dc.subject | cervical spondylotic myelopathy | - |
dc.subject | diffusion indices | - |
dc.subject | feature selection | - |
dc.subject | machine learning | - |
dc.title | Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning | - |
dc.type | Article | - |
dc.identifier.email | Hu, Y: yhud@hku.hk | - |
dc.identifier.authority | Hu, Y=rp00432 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1142/S0129065717500368 | - |
dc.identifier.pmid | 28830310 | - |
dc.identifier.scopus | eid_2-s2.0-85028324092 | - |
dc.identifier.hkuros | 289694 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1750036 | - |
dc.identifier.epage | 1750036 | - |
dc.identifier.isi | WOS:000423207800003 | - |
dc.publisher.place | Singapore | - |
dc.identifier.issnl | 0129-0657 | - |