Evidence map›Paper›PMID 41381839›Full record

ArticleNPJ precision oncology2025

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

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 and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Jan-Niklas EckardtDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany. jan-niklas.eckardt@uniklinikum-dresden.de.
Ishan SrivastavaDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Freya SchulzeDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Susann WinterDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Tim SchmittmannInstitute of Software and Multimedia Technology, TUD Dresden University of Technology, Dresden, Germany.
Sebastian RiechertElse Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Martin M K SchneiderDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Lukas ReichelDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Miriam Eva Helena GedigaDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Katja SockelDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Anas Shekh SulaimanDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Christoph RölligDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Frank KroschinskyDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Anne-Marie AsemissenDepartment of Hematology, Oncology and Bone Marrow Transplantation with Section of Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Christian PohlkampMunich Leukemia Laboratory, Munich, Germany.
Torsten HaferlachMunich Leukemia Laboratory, Munich, Germany.
Martin BornhäuserDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Karsten WendtElse Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
Jan Moritz MiddekeDepartment of Internal Medicine I, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID41381839
PMCPMC12808678

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.