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Publication Detail
Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Eshaghi A, Riyahi-Alam S, Saeedi R, Roostaei T, Nazeri A, Aghsaei A, Doosti R, Ganjgahi H, Bodini B, Shakourirad A, Pakravan M, Ghana'Ati H, Firouznia K, Zarei M, Azimi AR, Sahraian MA
  • Publication date:
    01/01/2015
  • Pagination:
    306, 314
  • Journal:
    NeuroImage: Clinical
  • Volume:
    7
  • Status:
    Published
Abstract
© 2015 The Authors. Published by Elsevier Inc. Neuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO, known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cognitive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearingwhite matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scoreswere the 3 most important modalities. Ourwork provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS.
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