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From gating to computational flow cytometry: Exploiting artificial intelligence for MRD diagnostics.
Riva, Giovanni; Luppi, Mario; Tagliafico, Enrico.
Afiliación
  • Riva G; Diagnostic Hematology and Clinical Genomics Laboratory, Department of Laboratory Medicine and Pathology, AUSL/AOU Modena, Modena, Italy.
  • Luppi M; Section of Hematology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, AOU Modena, Modena, Italy.
  • Tagliafico E; Diagnostic Hematology and Clinical Genomics Laboratory, Department of Laboratory Medicine and Pathology, AUSL/AOU Modena, Modena, Italy.
Br J Haematol ; 202(4): 715-717, 2023 08.
Article en En | MEDLINE | ID: mdl-37092558
ABSTRACT
The era of AI-based methods to improve flow cytometry diagnostics in haematology is now at the beginning. The study by Nguyen and colleagues explored an emerging machine learning approach to assess phenotypic MRD in chronic lymphocytic leukaemia patients, showing that such AI-driven computational analysis may represent a robust and feasible tool for advanced diagnostics of haematological malignancies. Commentary on Nguyen et al. Computational flow cytometry provides accurate assessment of measurable residual disease in chronic lymphocytic leukaemia. Br J Haematol 2023;202760-770.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Leucemia Linfocítica Crónica de Células B / Neoplasias Hematológicas Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Br J Haematol Año: 2023 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Leucemia Linfocítica Crónica de Células B / Neoplasias Hematológicas Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Br J Haematol Año: 2023 Tipo del documento: Article País de afiliación: Italia