From gating to computational flow cytometry: Exploiting artificial intelligence for MRD diagnostics

Author:

Riva Giovanni1ORCID,Luppi Mario2,Tagliafico Enrico1

Affiliation:

1. Diagnostic Hematology and Clinical Genomics Laboratory, Department of Laboratory Medicine and Pathology AUSL/AOU Modena Modena Italy

2. Section of Hematology, Department of Medical and Surgical Sciences University of Modena and Reggio Emilia, AOU Modena Modena Italy

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;202:760–770.

Publisher

Wiley

Subject

Hematology

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