AJCP CME: Review of flow cytometry findings and associated scoring approaches for identifying myelodysplastic syndrome and the future role of machine learning in improving the diagnostic algorithm

This AJCP article offers 1.0 continuing education (CME) credit that can be used to fulfill CC Part II requirements. Purchase of this CME activity provides gratis access to a PDF of the journal article for the learner’s personal use only.

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Intended Audience: Pathologists, Residents, Clinical Scientists, Pathologist Assistants

Upon completion of this activity, learners will be able to:

Summarize flow cytometric findings that are highly correlated with a diagnosis of Myelodysplastic Syndrome (MDS).
Compare and contrast reported flow...

Intended Audience: Pathologists, Residents, Clinical Scientists, Pathologist Assistants

Upon completion of this activity, learners will be able to:

Summarize flow cytometric findings that are highly correlated with a diagnosis of Myelodysplastic Syndrome (MDS).
Compare and contrast reported flow cytometric algorithms for diagnosing MDS.
Discuss the application of machine learning tools to the flow cytometric diagnosis of MDS, including potential benefits and gaps to implementation.

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