Jan‐Niklas Eckardt, Ishan Srivastava, Freya Schulze, Susann Winter, Tim Schmittmann, Sebastian Riechert, Martin Schneider, Lukas Reichel, Miriam Eva Helena Gediga, Katja Sockel, Anas Shekh Sulaiman, Christoph Röllig, Frank Kroschinsky, Anne-Marie Asemissen, Christian Pohlkamp, Torsten Haferlach, Martin Bornhäuser, Karsten Wendt, Jan Moritz Middeke
Cytomorphological assessment of bone marrow smears (BMS) is essential in the diagnosis of myelodysplastic neoplasms (MDS), yet manual evaluation is prone to inter-observer variability. We trained end-to-end deep learning models to distinguish between MDS, acute myeloid leukemia, and bone marrow donor BMS with high accuracy in internal tests and external validation. Occlusion sensitivity mapping revealed the high importance of nuclear structures beyond canonical dysplasia, demonstrating accurate, interpretable MDS detection without labor-intensive cell-level annotation.