Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: A magnetic resonance spectroscopy study based on machine learning Diferenciação de esclerose múltipla recorrente-remitente e progressiva secundária: Um estudo de ressonância magnética com espectroscopia baseado em aprendizado de máquina


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Ekşi Z., Çakiroğlu M., Öz C., Aralaşmak A., Karadeli H. H., Özcan M. E.

Arquivos de Neuro-Psiquiatria, vol.78, no.12, pp.789-796, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 78 Issue: 12
  • Publication Date: 2020
  • Doi Number: 10.1590/0004-282x20200094
  • Journal Name: Arquivos de Neuro-Psiquiatria
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, EMBASE, MEDLINE, Psycinfo, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.789-796
  • Keywords: Chronic progressive, Machine learning, Magnetic resonance spectroscopy, Multiple sclerosis, Multiple sclerosis, Multiple sclerosis, Relapsing-remitting
  • İstanbul Yeni Yüzyıl University Affiliated: Yes

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.