Detection and quantification of Babesia species intraerythrocytic parasites by flow cytometry

Patrick M. Vanderboom, Anisha Misra, Kyle G. Rodino, Allison R. Eberly, Jason D. Greenwood, Heather E. Morris, Felicity C. Norrie, Emily C. Fernholz, Bobbi S. Pritt, Andrew P. Norgan

Research output: Contribution to journalArticlepeer-review


Objectives: Recent work has demonstrated that automated fluorescence flow cytometry (FLC) is a potential alternative for the detection and quantification of Plasmodium parasites. The objective of this study was to apply this novel FLC method to detect and quantify Babesia parasites in venous blood and compare results to light microscopy and polymerase chain reaction methods. Methods: An automated hematology/malaria analyzer (XN-31; Sysmex) was used to detect and quantify B microti-infected red blood cells from residual venous blood samples (n = 250: Babesia positive, n = 170; Babesia negative, n = 80). As no instrument software currently exists for Babesia, qualitative and quantitative machine learning (ML) algorithms were developed to facilitate analysis. Results: Performance of the ML models was verified against the XN-31 software using P falciparum-infected samples. When applied to Babesia-infected samples, the qualitative ML model demonstrated an area under the curve (AUC) of 0.956 (sensitivity, 95.9%; specificity, 83.3%) relative to polymerase chain reaction. For valid scattergrams, the qualitive model achieved an AUC of 1.0 (sensitivity and specificity, 100%), while the quantitative model demonstrated an AUC of 0.986 (sensitivity, 94.4%; specificity, 100%). Conclusions: This investigation demonstrates that Babesia parasites can be detected and quantified directly from venous blood using FLC. Although promising, opportunities remain to improve the general applicability of the method.

Original languageEnglish
Pages (from-to)451-462
Number of pages12
JournalAmerican journal of clinical pathology
Issue number5
StatePublished - May 1 2024


  • Babesia
  • FLC
  • ML
  • flow cytometry
  • machine learning
  • malaria


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