Digital system augmented by artificial intelligence to interpret bone marrow samples for hematological disease diagnosis

Abstract

AbstractAnalysis 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.