dc.rights.license |
DOAJ Gold |
|
dc.date.accessioned |
2021-06-03T08:20:16Z |
|
dc.date.available |
2021-06-03T08:20:16Z |
|
dc.date.issued |
2020 |
|
dc.identifier.issn |
0004-282X |
|
dc.identifier.uri |
www.doi.org/10.1590/0004-282X20200094 |
|
dc.identifier.uri |
https://hdl.handle.net/20.500.12619/95118 |
|
dc.description |
Bu yayın 06.11.1981 tarihli ve 17506 sayılı Resmî Gazete’de yayımlanan 2547 sayılı Yükseköğretim Kanunu’nun 4/c, 12/c, 42/c ve 42/d maddelerine dayalı 12/12/2019 tarih, 543 sayılı ve 05 numaralı Üniversite Senato Kararı ile hazırlanan Sakarya Üniversitesi Açık Bilim ve Açık Akademik Arşiv Yönergesi gereğince açık akademik arşiv sistemine açık erişim olarak yüklenmiştir. |
|
dc.description.abstract |
Introduction: Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS) is clinically difficult, and developing the proposal presented in this study would contribute to the process. Objective: This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning methods. Methods: MR spectroscopy (MRS) was performed on a total of 91 participants, distributed into healthy controls (n=30), RRMS (n=36), and SPMS (n=25). Firstly, MRS metabolites were identified using signal processing techniques. Secondly, feature extraction was performed based on MRS Spectra. N-acetylaspartate (NM) was the most significant metabolite in differentiating MS types. Lastly, binary classifications (healthy controls-RRMS and RRMS-SPMS) were carried out according to features obtained by the Support Vector Machine algorithm. Results: RRMS cases were differentiated from healthy controls with 85% accuracy, 90.91% sensitivity, and 77.78% specificity. RRMS and SPMS were classified with 83.33% accuracy, 81.81% sensitivity, and 85.71% specificity. Conclusions: A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types. |
|
dc.description.sponsorship |
Sakarya University BAPKSakarya University [2015-50-02-012] |
|
dc.language |
English |
|
dc.language.iso |
İngilizce |
|
dc.publisher |
ASSOC ARQUIVOS NEURO- PSIQUIATRIA |
|
dc.relation.isversionof |
10.1590/0004-282X20200094 |
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
dc.subject |
PROTON MR SPECTROSCOPY |
|
dc.subject |
CEREBROSPINAL-FLUID ANALYSIS |
|
dc.subject |
BRAIN-TUMOR CLASSIFICATION |
|
dc.subject |
Multiple Sclerosis |
|
dc.subject |
Multiple Sclerosis |
|
dc.subject |
Relapsing-Remitting |
|
dc.subject |
Multiple Sclerosis |
|
dc.subject |
Chronic Progressive |
|
dc.title |
Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning |
|
dc.type |
Article |
|
dc.contributor.authorID |
Karadeli, Hasan/0000-0002-0470-8247 |
|
dc.contributor.authorID |
Ozcan, Emin/0000-0002-3220-6391 |
|
dc.identifier.volume |
78 |
|
dc.identifier.startpage |
789 |
|
dc.identifier.endpage |
796 |
|
dc.relation.journal |
ARQUIVOS DE NEURO-PSIQUIATRIA |
|
dc.identifier.issue |
12 |
|
dc.identifier.wos |
WOS:000600287400007 |
|
dc.identifier.doi |
10.1590/0004-282X20200094 |
|
dc.identifier.eissn |
1678-4227 |
|
dc.contributor.author |
Eksi, Ziya |
|
dc.contributor.author |
Cakiroglu, Murat |
|
dc.contributor.author |
Oz, Cemil |
|
dc.contributor.author |
Aralasmak, Ayse |
|
dc.contributor.author |
Karadeli, Hasan Huseyin |
|
dc.contributor.author |
Ozcan, Muhammed Emin |
|
dc.relation.publicationcategory |
Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı |
|
dc.identifier.pmıd |
33331515 |
|