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Myelodysplastic syndrome (MDS) is a rare haematological cancer characterized by abnormal development and maturation (dysplasia) of immature blood cells in the bone marrow, resulting in varying levels of cytopenia in the peripheral blood. The condition is typically suspected in patients with unexplained cytopenia. However, diagnosing MDS can be challenging and often requires multiple bone marrow samples. While dysplasia in the bone marrow is a hallmark of the disease, some patients exhibit cytopenia with only subtle morphological changes. These variations pose challenges to an accurate diagnosis and contribute to inter-observer variability, making consistent interpretation through traditional slide-based methods quite difficult.

This webcast explores the diagnostic potential of imaging flow cytometry (IFC) in MDS. The IFC technology merges high throughput single-cell analysis with detailed morphological information, enhancing both statistical accuracy and sensitivity beyond conventional microscopy methods. As such, IFC offers an attractive novel approach for an objective, standardized and reproducible method for dysplasia assessment. Moreover, recent advancements in machine learning and deep learning have opened new possibilities for data analysis, allowing for the development of classification models from IFC image data.

Learn about:

• Challenges in diagnosing myelodysplastic syndromes (MDS)
• Diagnostic potential of IFC in the context of dysplasia assessment in MDS
• Application of machine learning and deep learning to identify and quantify dysplastic cells in IFC image data

For Research Use Only. Not intended for use in diagnostic procedures.

This webcast has been produced by Cytek Biosciences, Inc who retains sole responsibility for content.

Moderator: Alison Halliday – Freelance Science, Writer
Presenter: Carina Agerbo Rosenberg – Senior Researcher, Department of Haematology, Aarhus University Hospital

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