Problem of differential diagnostics of neurodegenerative pathology
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Keywords

multiple sclerosis
Wilson–Konovalov’s disease
magnetic resonance imaging

How to Cite

Chernenko, M. (2020). Problem of differential diagnostics of neurodegenerative pathology. Medicine Today and Tomorrow, 84(3), 49–54. https://doi.org/10.35339/msz.2019.84.03.08

Abstract

The differential diagnosis of multiple sclerosis and WilsonKonovalov’s disease is an important clinical task, the timely appointment of adequate therapy depends on the correct solution of which. With the seemingly simple differential diagnosis of these diseases, in some cases, especially atypical, there are certain difficulties in making the correct diagnosis. Magnetic resonance imaging is a mandatory study in the structure of a comprehensive examination of patients with multiple sclerosis and Wilson–Konovalov’s disease. Conducting standardized magnetic resonance imaging scans performed on a high-field tomograph is mandatory in all cases of differential diagnosis in patients with demyelinating pathology of the central nervous system and neurodegenerative diseases. An analysis of the topography of focal changes in dynamics, an assessment of the rate of increase of atrophic changes is of great importance for the timely appointment of adequate treatment, which in turn directly affects the prognosis of the disease. The brain magnetic resonance imaging data from 30 patients with multiple sclerosis and 10 patients with Wilson–Konovalov’s disease who were treated at the GU INPN NAMS of Ukraine (Kharkov) were described. It is concluded that both multiple sclerosis and Wilson–Konovalov’s disease are chronic partially controlled diseases in which dynamic monitoring is extremely important, an integral part of which is the conduct of adequate neuroimaging. None of the methods for diagnosing multiple sclerosis and Wilson–Konovalov’s disease has independent diagnostic value, and diagnosis is possible only with a comprehensive analysis of clinical, laboratory, genetic and neuroimaging data.

https://doi.org/10.35339/msz.2019.84.03.08
PDF (Русский)

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