ABSTRACT

INTRODUCTION. Magnetic resonance imaging (MRI) of the central nervous system (CNS) is the method of choice in patients with clinical manifestations of multiple sclerosis (MS) to determine dissemination in space and time according to McDonald diagnostic criteria. Being an important diagnostic tool, tomography also acts as a method for further monitoring of the condition, including for assessing the subclinical course of the disease. The multiplicity of demyelination foci and the discrete nature of the course are an obstacle to a quick and accurate analysis of the volume of changes that have occurred. The automated system for analyzing MRI scans "Brain Snitch", developed by the Department of Nervous and Neurosurgical Diseases together with the Laboratory of Information and Computer Technologies of the Research Institute of the Belarusian State Medical University, allows tracking the current state of the lesion: the degree of activity of the pathological process, the size of the lesion, its localization and intensity.

AIM. Evaluation of the state of absolute brightness of active demyelination foci in patients with MS using the automated system "Brain Snitch", based on the work of artificial intelligence.

MATERIALS AND METHODS. Scans of various sequences of MRI studies (T1, T1 with contrast, T2, T2 FLAIR) of MRI studies of patients with MS. Active demyelination lesions visualized in T1 mode on MRI were compared with similar lesions in T2 and T2 FLAIR modes in the Brain Snitch program to obtain tabular values of absolute brightness (intensity equivalent, presented by the program using the artificial intelligence technology of the automated Brain Snitch program) of these lesions.

RESULTS AND DISCUSSION. Using the automated Brain Snitch program based on artificial intelligence technology, a tendency towards reduced absolute brightness of active demyelination lesions (p = 0.046) was found, compared with the absolute brightness values​​in T2 mode of lesions similar to inactive (not accumulating contrast agent) in T1 mode.

CONCLUSIONS. The tendency found in the course of the study to a reduced absolute brightness of active foci in the T2 mode may serve as a basis for the development of additional criteria for evaluating the activity of pathomorphological foci in multiple sclerosis in radiological and neurological practice.

KEYWORDS: Artificial intelligence, multiple sclerosis, magnetic resonance imaging, demyelination foci, absolute brightness

For citation: Shpakouski A.Yu., Mulitsa A.V., Blagochinnaya K.V. Possibilities of Artificial Intelligence in Characterizing Demyelination Foci in Patients with Multiple Sclerosis. Comorbidity Neurology. 2024; 1 (4): 38-43. https://doi.org/10.62505/3034-185x-2024-1-4-38-43

For correspondence: Aliaksandr Yu. Shpakouski, Faculty of General Medicine, Belarusian State Medical University, Minsk, Belarus, email: alexandr.shpakovski@gmail.com

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ADDITIONAL INFORMATION

Aliaksandr Yu. Shpakouski, Faculty of General Medicine, Belarusian State Medical University, Minsk, Belarus. E-mail: alexandr.shpakovski@gmail.com. ORCID: https://orcid.org/0009-0007-7193-3317

Anna V. Mulitsa, Faculty of General Medicine, Belarusian State Medical University, Minsk, Belarus. E-mail: amulitsa135246@yandex.by

Ksenia V. Blagochinnaya, Senior Lecturer, Department of Nerve and Neurosurgical Diseases, Belarusian State Medical University, Minsk, Belarus. E-mail: blagochinnaya00@mail.ru

Author contributions. All authors confirm their authorship in accordance with the international ICMJE criteria (all authors made a significant contribution to the development of the concept, the conduct of the study and the preparation of the article, read and approved the official version before publication). Shpakovsky A.Yu., Mulitsa A.V., Blagochinnaya K.V. – development of the article concept, collection of materials, preparation of materials, preparation of the article for publication.

Funding. The authors declare no external funding for the study.

Disclosure. The authors declare no other obvious or potential conflicts of interest related to the publication of this article.

The content is available under the Creative Commons Attribution 4.0 License.

©2024. Aliaksandr Yu. Shpakouski, Anna V. Mulitsa, Ksenia V. Blagochinnaya