V.G. Nikitaev1, T.G. Gevorkyan2, A.N. Pronichev3, T.N. Zabotina4, O.V. Nagornov5, A.A. Borunova6, A.S. Samofalova7, M.S. Torosyan8

1,3,4,8 National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) (Moscow, Russia)

2,4,6,8 Blokhin National Research Medical Center of Oncology, Ministry of Health of the Russian Federation (Moscow, Russia)

7 Federal State Budgetary Educational Institution of Higher Education "Voronezh State Technical University" (Voronezh, Russia)

1 VGNikitayev@mephi.ru, 2 t.gevorkian@ronc.ru, 3 anpronichev@mephi.ru, 4 tnz@ronc.ru, 5 OVNagornov@mephi.ru, 6 a.borunova@ronc.ru, 7 malinka.alya8@yandex.ru, 8 torosyan.marlen@yandex.ru

The development of technologies has not affected the process of analyzing laser flow cytometry data, which is performed manually by doctors of clinical and laboratory diagnostics. The accuracy and speed of this visual analysis depends on the specialist's experience. In addition, the process is laborious and subjective. Development of a method for multiparametric cluster analysis of flow cytometry data to reduce labor intensity and increase the objectivity of the research results. The scientific substantiation of the proposed original method of multiparametric cluster analysis of laser flow cytometry data has been carried out. The method is based on data preparation, adaptive clustering, filtering of erroneous clusters, analysis of cluster consistency based on a set of parameters, and recognition of leukocyte populations. The experiment demonstrated an average accuracy of recognition of leukocyte populations of 93%.

Nikitaev V.G., Gevorkyan T.G., Pronichev A.N., Zabotina T.N., Nagornov O.V., Borunova A.A., Samofalova A.S., Torosyan M.S. Artificial intelligence in Russian laser flow cytometry systems: recognition of leukocyte populations. A new clustering method // Biomedical Radioelectronics. 2026. V. 29. № 6. P. 8−13. DOI: https://doi.org/10.18127/j15604136-202606-02

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