AI cytometry publications

ESMO AI 2025 poster. This work on AI cytometry was led by hema.to’s partners at the HpH Institut für Pathologie und Hämatopathologie Hamburg and Pathodiagnostik Berlin MVZ GmbH.

The retrospective comparison across more than 6,500 peripheral-blood samples shows high concordance with conventional laboratory results across major subpopulations, robustness across multiple panels, workflows, and laboratory locations, and where further harmonization is needed.

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Clinical study white paper. hema.to was clinically validated in an international, four-center clinical trial. Blood cancer diagnoses were made twice as fast, with 8% more sensitivity and 10% more specificity, using hema.to’s decision-support software.

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Cell Patterns paper. Nanditha Mallesh and co-authors describe how knowledge transfer enhances AI performance in Cell Patterns.

The collaboration between hema.to’s team, the Munich Leukemia Lab, Charité, and additional university clinics showed that data from multiple laboratories can be combined to increase the clinical performance of AI models for each laboratory.

Cytometry A paper. hema.to’s team collaborated with experienced hematologists and the University of Bonn on this Cytometry A study.

Zhao et al. showed that deep learning can aid routine clinical workflows using cytometry data.

Blood paper II. Nanditha Mallesh and co-authors presented this 2019 Blood paper.

The study showed that combining multiple datasets improves diagnostic performance through an approach called “knowledge transfer.”

Blood paper I. A collaboration between hema.to, the Munich Leukemia Lab, and the University of Bonn produced this 2019 Blood paper.

The paper shows that AI can support hematologists in routine blood cancer diagnosis workflows by providing highly reliable recommendations.