Open Access

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

  • Authors:
    • Eleftherios E. Kontopodis
    • Efrosini Papadaki
    • Eleftherios Trivizakis
    • Thomas G. Maris
    • Panagiotis Simos
    • Georgios Z. Papadakis
    • Aristidis Tsatsakis
    • Demetrios A. Spandidos
    • Apostolos Karantanas
    • Kostas Marias
  • View Affiliations

  • Published online on: August 9, 2021     https://doi.org/10.3892/etm.2021.10583
  • Article Number: 1149
  • Copyright: © Kontopodis et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Computer‑aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence‑based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.
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October-2021
Volume 22 Issue 4

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Copy and paste a formatted citation
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Spandidos Publications style
Kontopodis EE, Papadaki E, Trivizakis E, Maris TG, Simos P, Papadakis GZ, Tsatsakis A, Spandidos DA, Karantanas A, Marias K, Marias K, et al: Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review). Exp Ther Med 22: 1149, 2021
APA
Kontopodis, E.E., Papadaki, E., Trivizakis, E., Maris, T.G., Simos, P., Papadakis, G.Z. ... Marias, K. (2021). Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review). Experimental and Therapeutic Medicine, 22, 1149. https://doi.org/10.3892/etm.2021.10583
MLA
Kontopodis, E. E., Papadaki, E., Trivizakis, E., Maris, T. G., Simos, P., Papadakis, G. Z., Tsatsakis, A., Spandidos, D. A., Karantanas, A., Marias, K."Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)". Experimental and Therapeutic Medicine 22.4 (2021): 1149.
Chicago
Kontopodis, E. E., Papadaki, E., Trivizakis, E., Maris, T. G., Simos, P., Papadakis, G. Z., Tsatsakis, A., Spandidos, D. A., Karantanas, A., Marias, K."Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)". Experimental and Therapeutic Medicine 22, no. 4 (2021): 1149. https://doi.org/10.3892/etm.2021.10583