Revolution in flow cytometry: using artificial intelligence for data processing and interpretation
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...
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| Format: | Article |
| Language: | English |
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Medical University of Gdańsk
2025-07-01
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| Series: | European Journal of Translational and Clinical Medicine |
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| Online Access: | https://ejtcm.gumed.edu.pl/articles/199793.pdf |
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| author | Szymon Bierzanowski Szymon Bierzanowski Krzysztof Pietruczuk |
| author_facet | Szymon Bierzanowski Szymon Bierzanowski Krzysztof Pietruczuk |
| author_sort | Szymon Bierzanowski |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ec37daff447492094bd225fb074ec3a |
| institution | Kabale University |
| issn | 2657-3148 2657-3156 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Medical University of Gdańsk |
| record_format | Article |
| series | European Journal of Translational and Clinical Medicine |
| spelling | doaj-art-0ec37daff447492094bd225fb074ec3a2025-08-20T03:35:32ZengMedical University of GdańskEuropean Journal of Translational and Clinical Medicine2657-31482657-31562025-07-01818396199793Revolution in flow cytometry: using artificial intelligence for data processing and interpretationSzymon Bierzanowski0https://orcid.org/0009-0008-0893-6731Szymon Bierzanowski1https://orcid.org/0009-0008-0893-6731Krzysztof Pietruczuk2https://orcid.org/0000-0001-7469-9254Division of Biostatistics and Neural Networks, Medical University of Gdańsk, PolandFaculty of Applied Physics and Mathematics, Gdańsk University of Technology, PolandDivision of Biostatistics and Neural Networks, Medical University of Gdańsk, PolandFlow 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.https://ejtcm.gumed.edu.pl/articles/199793.pdfflow cytometrymachine learningdata analysisai algorithmsautomation |
| spellingShingle | Szymon Bierzanowski Szymon Bierzanowski Krzysztof Pietruczuk Revolution in flow cytometry: using artificial intelligence for data processing and interpretation European Journal of Translational and Clinical Medicine flow cytometry machine learning data analysis ai algorithms automation |
| title | Revolution in flow cytometry: using artificial intelligence for data processing and interpretation |
| title_full | Revolution in flow cytometry: using artificial intelligence for data processing and interpretation |
| title_fullStr | Revolution in flow cytometry: using artificial intelligence for data processing and interpretation |
| title_full_unstemmed | Revolution in flow cytometry: using artificial intelligence for data processing and interpretation |
| title_short | Revolution in flow cytometry: using artificial intelligence for data processing and interpretation |
| title_sort | revolution in flow cytometry using artificial intelligence for data processing and interpretation |
| topic | flow cytometry machine learning data analysis ai algorithms automation |
| url | https://ejtcm.gumed.edu.pl/articles/199793.pdf |
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