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

Flow cytometry provides additional information that can help in diagnosis of MDS. Multiple scoring systems now exist that have significant potential to improve the standard approach to diagnosing MDS. Although requiring further development and validation, machine learning methods appear promising as an even more sensitive, specific, and rapid approach to using clinical flow cytometry for identification of MDS than the various prior reported scoring methods. We look forward to furthering improvements in flow cytometry for evaluation of patients with MDS, particularly through the developing use of machine learning approaches and other computational methods.