AI-based image classification algorithms for Leukaemia diagnostics and hematologic cytomorphology: From single cells to molecular features

Abstract

Due to the progress of image analysis and classification systems in recent years, algorithms have been developed that support morphologic examination of both single cells and tissue samples. These algorithms are typically developed using data-driven strategies, which require comprehensive, large-scale datasets. In the diagnostic workup of hematopoietic malignancies, cytomorphologic examination and differentiation represents a key first step. In recent years, the availability of large-scale, high-quality datasets of single leukocytes from peripheral blood and bone marrow has led to the development of diagnostic support algorithms for this modality. These methods not only allow a faster and more consistent classification of diagnostically relevant cell types, but also pave the way for integrated analysis of cytomorphologic and molecular findings.