Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases

Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and...

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Published inMicroscopy and microanalysis Vol. 30; no. 1; pp. 151 - 159
Main Authors Bermejo-Peláez, David, Rueda Charro, Sandra, García Roa, María, Trelles-Martínez, Roberto, Bobes-Fernández, Alejandro, Hidalgo Soto, Marta, García-Vicente, Roberto, Morales, María Luz, Rodríguez-García, Alba, Ortiz-Ruiz, Alejandra, Blanco Sánchez, Alberto, Mousa Urbina, Adriana, Álamo, Elisa, Lin, Lin, Dacal, Elena, Cuadrado, Daniel, Postigo, María, Vladimirov, Alexander, Garcia-Villena, Jaime, Santos, Andrés, Ledesma-Carbayo, María Jesús, Ayala, Rosa, Martínez-López, Joaquín, Linares, María, Luengo-Oroz, Miguel
Format Journal Article
LanguageEnglish
Published England Oxford University Press 07.03.2024
Subjects
Algorithms
Artificial Intelligence
Bone Marrow
Cell differentiation
Hematologic Diseases - diagnosis
Hematological diseases
Hematology
Humans
Microscopy
Optical microscopes
Variability
bone marrow aspirates
artificial intelligence (AI)
digital microscopy
differential cell counting (DCC)
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Summary:Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.
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ISSN:1431-9276
1435-8115
1435-8115
DOI:10.1093/micmic/ozad143