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<p>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.</p>

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Image-based explainable artificial intelligence accurately identifies myelodysplastic neoplasms beyond conventional signs of dysplasia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月11日

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  • Jan-Niklas Eckardt,
  • Ishan Srivastava,
  • Freya Schulze,
  • Susann Winter,
  • Tim Schmittmann,
  • Sebastian Riechert,
  • Martin M. K. 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

摘要

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.