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Dasdelen, M.F. ; Kukuljan; I. ; Lienemann, P. ; Ozlugedik, F. ; Sadafi, A. ; Hehr, M. ; Spiekermann, K.* ; Pohlkamp, C.* ; Marr, C.

AI-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort.

Leukemia, DOI: 10.1038/s41375-026-02934-1 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Hematological malignancies represent a wide range of disease entities, most of which arise from dysfunctional proliferation and differentiation of hematopoietic stem and progenitor cells in the bone marrow [1]. Diagnosis requires integration of cytomorphology, molecular genetics, and immunophenotyping from blood or bone marrow. Unlike bone marrow aspiration, assessing cytomorphology in a blood smear is fast, minimally invasive, and provides information on differential cell counts and morphological abnormalities that guide follow-up diagnostic pathways. However, conventional peripheral blood smear analysis involves labor-intensive manual examination of hundreds of cells, which is subject to inter-observer variability. Previous work explored machine-learning for single-cell classification [2, 3], and disease detection [4,5,6,7,8,9] on curated cohorts. Systematic evaluation across multiple malignancies at their natural clinical distribution remains unexplored.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 0887-6924
e-ISSN 1476-5551
Zeitschrift Leukemia
Verlag Springer
Verlagsort Campus, 4 Crinan St, London, N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Förderungen EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)