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Fifteen radiologists independently evaluated fifty non-contrast brain computed tomography of patients with middle cerebral artery (MCA) stroke. Within-group agreement was either negligible or moderate, and the best inter-expert agreement was observed between neuroradiologists with 1 to 3 years of experience. Our study allows us to conclude that ASPECTS is not an ideal tool for standardizing the diagnosis of MCA stroke, therefore, it is necessary to develop new models of standardization systems, as well as the use of hydride diagnostics, including automatic analysis.

Introduction. The Alberta stroke programme early CT score (ASPECTS) was developed for a unified approach to the diagnosis of Acute Ischemic Stroke. ASPECTS is currently used as a standard method for assessment of ischemic volumes in the anterior cerebral circulation. However, the scale is not fully standardized, which is a source of intersubject variability.The purpose of the review is to gain an understanding the advantages and limitations of the ASPECTS scale, as well as the level of inter-expert and intra-expert agreement.Results. A literary analysis demonstrates most researchers have identified many factors that affect both the interpretation and assessment of the distribution of ischemic changes by ASPECTS. These signs are diverse and include a wide range of parameters: from methodological standardization to personal factors of experts. Also, studies on the effectiveness of the ASPECTS scale showed quite heterogeneous results, which reflect a wide degree of variability in inter-expert agreement.Conclusion. The ASPECTS is a systematic, reliable and practical method that is widely used in modern clinical practice. However, the possibility of variability of expert assessments is the main limitation of its application. The pronounced variety of results and the heterogeneity of intrasubject variability does not currently allow us to consider this scale as a truly reliable version of a standardized assessment and may affect the further treatment process. To solve this problem, it looks promising to introduce into clinical practice the methods of semi-automatic and automatic processing of CT images using artificial intelligence systems. But for the full acceptance of such systems into clinical practice, their wide clinical approbation on independent sets of different data is necessary.