employee from 01.01.2006 to 01.01.2026
Yelabuga, Kazan, Russian Federation
student
Naberezhnye Chelny, Kazan, Russian Federation
UDC 338.2
UDC 004.932
The research is devoted to the development and comprehensive evaluation of a deep learning algorithm for automating a key stage in the diagnosis of acute lymphoblastic leukemia (ALL) — morphological analysis of blood cells. The aim of the work is to bridge the gap between the technical validation of AI and the assessment of its economic and managerial potential. Based on a representative clinical dataset (15,135 images), the EfficientNetB3 model achieved 97.5% accuracy, 96.2% sensitivity, and 97.8% specificity (AUC-ROC = 0.987). The key result is a quantitative assessment of the operational effect: economic and temporal simulation has shown that using the algorithm as a pre-selection system can reduce the time of routine screening by a specialist by 70-85%. This frees up the most valuable resource — the expert's time — to solve complex problems. It is concluded that the implementation of such solutions leads to three main results: improving the operational efficiency of the laboratory, standardizing the quality of diagnostics, and transforming the specialist's work towards higher added value. Thus, the work demonstrates that AI technologies are not just a tool for improving accuracy, but a strategic investment in creating a more sustainable and cost-effective healthcare system.
acute lymphoblastic leukemia, artificial intelligence, deep learning, diagnostics, healthcare economics, operational efficiency.
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9. Application of machine learning algorithms to develop a model for predicting the survival rate of lung cancer patients in the Republic of Kazakhstan / V. A. N. Makarov, D. N. R. Kaidarova, S. N. E. N. Esentayeva [et al.] / / Oncology and radiology of Kazakhstan. – 2022. – № 3(65). - C. 13. 4-11. – DOIhttps://doi.org/10.52532/2521-6414-2022-3-65-4-11. – EDN VSAJZG.
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