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Revolution in flow cytometry: using artificial intelligence for data processing and interpretation

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Bierzanowski S., Pietruczuk K.
European Journal of Translational and Clinical Medicine
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2025
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vol. 8
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issue 1
83-96
EN
Flow cytometry (FC) represents a pivotal technique in the domain of biomedical research, facilitating the analysis of the physical and biochemical properties of cells. The advent of artificial intelligence (AI) algorithms has marked a significant turning point in the processing and interpretation of cytometric data, facilitating more precise and efficient analysis. The application of key AI algorithms, including clustering techniques (unsupervised learning), classification (supervised learning) and advanced deep learning methods, is becoming increasingly prevalent. Similarly, multivariate analysis and dimension reduction are also commonly attempted. The integration of advanced AI algorithms with FC methods contributes to a better understanding and interpretation of biological data, opening up new opportunities in research and clinical diagnostics. However, challenges remain in optimising the algorithms for the specificity of the cytometric data and ensuring their interpretability and reliability.
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