%0 Journal Article
%A Bermejo Peláez, David
%A Rueda Charro, Sandra 
%A García Roa, María 
%A Trelles Martínez, Roberto 
%A Bobes-Fernández, Alejandro 
%A Hidalgo Soto, Marta 
%A García-Vicente, Roberto 
%A Morales Fernández, María Luz 
%A Rodríguez García, Alba 
%A Ortiz Ruiz, Alejandra 
%A Blanco Sánchez, Alberto 
%A Mousa Urbina, Adriana 
%A Álamo García-Donas, Elisa 
%A Lin, Lin
%A Dacal Picazo, Elena 
%A Cuadrado Sánchez, Daniel 
%A Postigo Camps, María 
%A Vladimirov, Alexander 
%A García-Villena, Jaime 
%A Santos Torres, Andrés 
%A Ledesma-Carbayo, Maria Jesús 
%A Ayala Díaz, Rosa María
%A Martínez López, Joaquín 
%A Linares Gómez, María
%A Luengo Oroz, Miguel 
%T Digital Microscopy Augmented by Artificial Intelligence toInterpret Bone Marrow Samples for Hematological Diseases 
%D 2022
%@ 1431-9276
%U https://hdl.handle.net/20.500.14352/101540
%X Analysis of bone marrow aspirates (BMA) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on visual examination of the 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 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 (DCC) process and reduces inter-observer 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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