Neuromyelitis Optica Spectrum Disorder and Other Non–Multiple Sclerosis Central Nervous System Inflammatory Diseases

2019 ◽  
Vol 25 (3) ◽  
pp. 815-844 ◽  
Author(s):  
Eoin P. Flanagan
2019 ◽  
Vol 11 (1) ◽  
pp. 40-44
Author(s):  
Enrique Gomez‐Figueroa ◽  
Christian Garcia‐Estrada ◽  
Adriana Casallas-Vanegas ◽  
Indhira Zabala-Angeles ◽  
Ramon Lopez-Martinez ◽  
...  

2020 ◽  
Vol 11 ◽  
Author(s):  
Hyunjin Kim ◽  
Youngin Lee ◽  
Yong-Hwan Kim ◽  
Young-Min Lim ◽  
Ji Sung Lee ◽  
...  

Background: Differentiating neuromyelitis optica spectrum disorder (NMOSD) from multiple sclerosis (MS) is crucial in the field of diagnostics because, despite their similarities, the treatments for these two diseases are substantially different, and disease-modifying treatments for MS can worsen NMOSD. As brain magnetic resonance imaging (MRI) is an important tool to distinguish the two diseases, extensive research has been conducted to identify the defining characteristics of MRI images corresponding to these two diseases. However, the application of such research in clinical practice is still limited. In this study, we investigate the applicability of a deep learning-based algorithm for differentiating NMOSD from MS.Methods: In this study, we included 338 participants (213 patients with MS, 125 patients with NMOSD) who visited the Asan medical center between February 2009 and February 2020. A 3D convolutional neural network, which is a deep learning-based algorithm, was trained using fluid-attenuated inversion recovery images and clinical information of the participants. The performance of the final model in differentiating NMOSD from MS was evaluated and compared with that of two neurologists.Results: The deep learning-based model exhibited an area under the receiver operating characteristic curve of 0.82 (95% CI, 0.75–0.89). It differentiated NMOSD from MS with an accuracy of 71.1% (sensitivity = 87.8%, specificity = 61.6%), which is comparable to that exhibited by the neurologists. The intra-rater reliability of the two neurologists was moderate (κ = 0.47, 0.50), which was in contrast with the consistent classification of the deep learning-based model.Conclusion: The proposed model was verified to be capable of differentiating NMOSD from MS with accuracy comparable to that of neurologists, exhibiting the advantage of consistent classification. As a result, it can aid differential diagnosis between two important central nervous system inflammatory diseases in clinical practice.


2021 ◽  
pp. 014556132110533
Author(s):  
Kuan-Ling Lin ◽  
Ching-Yu Yang ◽  
Wen-Ko Su

Neuromyelitis optica spectrum disorder (NMOSD) is an uncommon antibody-mediated disease of the central nervous system. Its classic presentation includes long segments of spinal cord inflammation, optic neuritis with or without intractable vomiting, and hiccups. Here, we described a case of a 39-year-old woman with an atypical presentation of vertigo, which was finally diagnosed as NMOSD by a positive serum aquaporin-4 antibody.


2021 ◽  
pp. 540-547
Author(s):  
W. Oliver Tobin

Multiple sclerosis is the most common idiopathic inflammatory demyelinating disease of the central nervous system (CNS), with a prevalence of 1 in 500 to 1 in 2,000 people, depending on geography and various other factors. Idiopathic inflammatory demyelinating diseases are a group of related disorders that include acute disseminated encephalomyelitis, neuromyelitis optica spectrum disorder, and myelin oligodendrocyte glycoprotein–immunoglobulin G–associated CNS demyelinating disease.


2021 ◽  
Vol 51 ◽  
pp. 102886
Author(s):  
Ricardo Alonso ◽  
Berenice Silva ◽  
Orlando Garcea ◽  
Patricio E. Correa Diaz ◽  
Giordani Rodrigues dos Passos ◽  
...  

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