What type of study is this?

This is a Validation Study study.

What question did this study set out to answer?

This research aims to enhance the diagnosis of myelodysplastic neoplasms using artificial intelligence techniques.

December 11, 2025Open Access

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

Key Points

This research aims to enhance the diagnosis of myelodysplastic neoplasms using artificial intelligence techniques.
Developed deep learning models for analysis of bone marrow smears
Trained models to distinguish between myelodysplastic neoplasms, acute myeloid leukemia, and donor samples
Utilized occlusion sensitivity mapping to interpret model decisions
Achieved high accuracy in distinguishing myelodysplastic neoplasms from other conditions
Revealed nuclear structures beyond traditional dysplasia criteria contributed significantly to detection
Demonstrated potential for interpretable diagnosis without extensive manual annotation

Abstract

Abstract 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.

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

Key Points

Abstract

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