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

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dc.contributor.authorBierzanowski, Szymon
dc.contributor.authorPietruczuk, Krzysztof
dc.contributor.organizationDivision of Biostatistics and Neural Networks, Medical University of Gdańsk, Poland
dc.contributor.organizationFaculty of Applied Physics and Mathematics, Gdańsk University of Technology, Poland
dc.date.accessioned2025-07-31T12:09:27Z
dc.date.available2025-07-31T12:09:27Z
dc.date.issued2025
dc.description.abstractFlow 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.en
dc.identifier.citationBierzanowski S, Pietruczuk K. Revolution in flow cytometry: using artificial intelligence for data processing and interpretation. Eur J Transl Clin Med. 2025;8(1):83-96
dc.identifier.doi10.31373/ejtcm/199793
dc.identifier.issn2657-3148
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/26031
dc.language.isoen
dc.publisherMedical University of Gdansk
dc.relation.ispartofseries8; 1
dc.rightsUznanie autorstwa-Na tych samych warunkach 4.0 Międzynarodoween
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.sourceEuropean Journal of Translational and Clinical Medicine
dc.subjectflow cytometryen
dc.subjectmachine learningen
dc.subjectdata analysisen
dc.subjectAI algorithmsen
dc.subjectautomationen
dc.titleRevolution in flow cytometry: using artificial intelligence for data processing and interpretationen
dc.typearticle
dc.type.versionpublishedVersion
person.identifier.orcidBierzanowski, Szymon [0009-0008-0893-6731]
person.identifier.orcidPietruczuk, Krzysztof [0000-0001-7469-9254]
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