Objective: to study the dynamic and patterns of atherosclerotic changes in the aortic arch using computed tomography data.
Material and methods. The computed tomography data of 158 patients obtained at the Philips Ingenuity CT scanner without contrast were studied. The aortic arch was divided into three parts: Part I – up to the brachiocephalic artery, Part II – up to the left subclavian artery, and Part III – distal to the left subclavian artery. The position of the local atherosclerotic lesions was determined by dividing the aorta into 24 segments. The data were grouped and statistically analyzed.
Results. Three age groups were identified: Group 1 – up to 55 years, Group 2 – 56–70 years, and Group 3 – over 70 years. The severity of aortic wall changes was assessed quantitatively by calculating the total number of affected segments among participants in each group. The significance level was p<0.05. Similar patterns of aortic atherosclerosis were found, but the location was not related to gender. The least number of lesions was in Part I of the arch, and the greatest was in Part III. There was an increase with age.
Conclusion. Atherosclerotic disease of the aortic arch is most commonly found in the upper half (segments 22–24) of Part I. In Part II, it was more frequent in the right half (segments 15–22). In Part III, the lesions were most pronounced in lower segments and on the left (segments 6–14). The location of lesions in men and women was slightly different.

Developmental venous anomalies (DVA) are among the most common malformations, accounting for about 55% of all cerebral vascular anomalies. Most of them are asymptomatic, detected incidentally by brain magnetic resonance imaging and do not require treatment. Complications in DVA are rare. Cases of DVAassociated hemorrhage, venous congestion, and epilepsy have been described in literature; however, most cases of complications are due to other malformations, such as cavernous and lymphatic malformations, focal cortical dysplasias. We present a rare clinical case of infratentorial venous infarction resulting from DVA thrombosis, which was triggered by thrombosis in contralateral sigmoid and transverse venous sinuses.

Levamisole is an antihelminthic drug that is widely used in clinical practice. One of its rare side effects is the development of levamisole-associated multifocal inflammatory leukoencephalopathy, which progresses 2–8 weeks after treatment and is characterized by brain demyelination and white matter damage. The article describes a case of levamisole-induced leukoencephalopathy, which should be differentiated from multiple sclerosis, acute disseminated encephalomyelitis, and neuromyelitis optica.

Artificial intelligence (AI) is currently developing very efficiently, and its applications are valuable in many fields of science, including medicine, mainly because of its ability to ensure accuracy, objectivity and automation, in particular, in the diagnostic process. Rapid development of diagnostic technologies provides an opportunity to introduce innovative solutions into modern medicine through the use of AI, which makes it possible to relieve medical workers by speeding up the diagnostic process and improving its quality as well as effectiveness of subsequent special treatment based on its results. This review briefly presents the current state of knowledge and a number of existing AI models applied in everyday practice in medical imaging. AI has great potential to transform X-ray diagnostics and other areas of medicine, especially in the analysis of medical images. Despite the difficulties associated with AI implementation in practice, such as the need for proper staff training and ethical issues, the advantages of its application are very significant. AI can help improve diagnostic accuracy, speed up the diagnostic process itself, and reduce medical costs. Further development of AI technologies combined with the constant cooperation between Russian AI developers and medical professionals will contribute to even greater advances in healthcare, which will undoubtedly benefit both patients and staff of medical institutions.

Background. In current clinical practice, the information contained in computed tomography (CT) images of lung cancer is not used to its full extent – only a few semantic characteristics (e.g. size, contours, nature of contrast agent accumulation, etc.). Today, researchers are attempting to transform CT image data into quantitative indicators describing the shape and texture of lung cancer, as well as to link these indicators with clinical data. This approach is called “radiomics” and is a developing field in medicine.
Objective: to analyze publications on differential diagnosis of non-small cell lung cancer (NSCLC) using texture analysis as well as to assess the possibilities and prospects of this method in increasing information content of CT studies.
Material and methods. The literature review presents data obtained from available sources in PubMed, ScienceDirect and Google Scholar databases, published up to and including the end of 2024, found using the key words and phrases in Russian and English languages: “NSCLC”, “lung adenocarcinoma”, “squamous cell lung cancer”, “computed tomography”, “radiomics”, “texture analysis”, “differential diagnostics”.
Results. The literature review describes the methods of texture analysis at all stages. Based on the results of the studied scientific works, the authors conclude that the use of texture analysis allows non-invasively predicting the histological form of NSCLC with sensitivity 72–83%, specificity 67–92%, and accuracy 74–86%. Conclusion. The use of texture analysis, according to published studies, is a promising method for differential diagnosis of histological forms of NSCLC (up to AUC ~0.7–0.9), however, the difference in methods and the lack of standardization of texture analysis require additional research.