Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning
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...
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| author | Ziya EKŞİ Murat ÇAKIROĞLU Cemil ÖZ Ayse ARALAŞMAK Hasan Hüseyin KARADELİ Muhammed Emin ÖZCAN |
| author_facet | Ziya EKŞİ Murat ÇAKIROĞLU Cemil ÖZ Ayse ARALAŞMAK Hasan Hüseyin KARADELİ Muhammed Emin ÖZCAN |
| author_sort | Ziya EKŞİ |
| collection | DOAJ |
| 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 (NAA) 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. |
| format | Article |
| id | doaj-art-dd4790e73876411abe4acb0febd1a2f8 |
| institution | Kabale University |
| issn | 1678-4227 |
| language | English |
| publisher | Thieme Revinter Publicações |
| record_format | Article |
| series | Arquivos de Neuro-Psiquiatria |
| spelling | doaj-art-dd4790e73876411abe4acb0febd1a2f82025-08-20T03:25:04ZengThieme Revinter PublicaçõesArquivos de Neuro-Psiquiatria1678-422710.1590/0004-282x20200094Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learningZiya EKŞİMurat ÇAKIROĞLUCemil ÖZAyse ARALAŞMAKHasan Hüseyin KARADELİMuhammed Emin ÖZCANABSTRACT 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 (NAA) 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.http://www.scielo.br/pdf/anp/v78n12/1678-4227-anp-78-12-789.pdfMultiple SclerosisMultiple Sclerosis, Relapsing-RemittingMultiple Sclerosis, Chronic ProgressiveMagnetic Resonance SpectroscopyMachine Learning |
| spellingShingle | Ziya EKŞİ Murat ÇAKIROĞLU Cemil ÖZ Ayse ARALAŞMAK Hasan Hüseyin KARADELİ Muhammed Emin ÖZCAN Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning Arquivos de Neuro-Psiquiatria Multiple Sclerosis Multiple Sclerosis, Relapsing-Remitting Multiple Sclerosis, Chronic Progressive Magnetic Resonance Spectroscopy Machine Learning |
| title | Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning |
| title_full | Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning |
| title_fullStr | Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning |
| title_full_unstemmed | Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning |
| title_short | Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning |
| title_sort | differentiation of relapsing remitting and secondary progressive multiple sclerosis a magnetic resonance spectroscopy study based on machine learning |
| topic | Multiple Sclerosis Multiple Sclerosis, Relapsing-Remitting Multiple Sclerosis, Chronic Progressive Magnetic Resonance Spectroscopy Machine Learning |
| url | http://www.scielo.br/pdf/anp/v78n12/1678-4227-anp-78-12-789.pdf |
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