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Evaluation of Efficacy of White Blood Cell Identification in Peripheral Blood by Automated Scanning of Stained Blood Smear Images with Variable Magnification

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Biomedical Engineering Aims and scope

Development of blood smear scanner analyzers is a new promising trend in designing devices for blood cell morphology examination. The efficacy of using the Vision Hema® Ultimate scanner analyzer for differential white blood cell count and identification of nucleated blood cells was evaluated. The average time required to analyze a single cover-glass preparation was found to be (108 ± 17)″. There was no statistically significant difference between the results of differential white blood cell count performed manually and using the Vision Hema® Ultimate device (p > 0.05); the results were virtually identical. The Spearman’s correlation coefficient was highest for segmented neutrophils, lymphocytes and eosinophils (0.9638%, 0.9342%, and 0.9172%, respectively). For monocytes and basophils it was 0.9047% and 0.7613%, respectively, while its lowest values were observed for immature myeloid cells. The Vision Hema® Ultimate blood smear scanner analyzer was shown to provide high accuracy and productivity of differential white blood cell count in peripheral blood smears.

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References

  1. Pierre, R. V., “Peripheral blood film review. The demise of the eye count leukocyte differential,” Clin. Lab. Med., 22, No. 1, 279-297 (2002).

    Article  Google Scholar 

  2. Chabot-Richards, D. S. and George, T. I., “White blood cell counts: Reference methodology,” Clin. Lab. Med., 35, No. 1, 11-24 (2015).

  3. Da Costa, L., “Digital image analysis of blood cells,” Clin. Lab. Med., 35, No. 1, 105-122 (2015).

  4. Ceelie, H., Dinkelaar, R. B., and van Gelder, W. “Examination of peripheral blood films using automated microscopy. Evaluation of Diffmaster Octavia and Cellavision DM 96,” J. Clin. Pathol., 60, 72-79 (2006).

    Article  Google Scholar 

  5. Koepke, J. A., Dotson, M. A., and Shifman, M. A., “A critical evaluation of the manual/visual differential leukocyte counting method,” Blood Cells, 11, No. 2, 173-186 (1985).

    Google Scholar 

  6. Green, R. and Wachsmann-Hogiu, S., “Development, history and future of automated cell counters,” Clin. Lab. Med., 35, No. 1, 1-10 (2015).

    Article  Google Scholar 

  7. Medovyi, V. S., Parpara, A. A., Pyatnitskii, A. M., and Sokolinskii, B. Z., “Robotic microscopy introduces the quality standard for blood smear tests,” Klin. Lab. Diagn., No. 9, 40-44 (2009).

  8. Medovyi, V. S., Pyatnitskii, A. M., Sokolinskii, B. Z., and Balugyan, R. Sh., “State-of-the-art capabilities of robotic microscopy for automation of tests and laboratory telemedicine (analytical review),” Klin. Lab. Diagn., No. 10, 32, 41-43 (2012).

  9. Plyasunova, S. A., Balygyan, R. Sh., Khmel’nitskii, K. E., Medovyi, V. S., Parpara, A. A., Pyatnitskii, A. M., Sokolinskii, B. Z., Dem’yanov, V. L., and Nikolaenko, D. S., “Automated methods for microscopic examination of blood smears: Clinical tests of the MEKOS-Ts2 system,” Klin. Lab. Diagn., No. 10, 21-24, 33-39 (2006).

  10. Sosnin, D. Yu., Nenasheva, O. Yu., Falkov, B. F., and Trusheva, L. A., “The effect of blood smear preparation on the operation of the automated blood smear analysis system Vision Hema,” Klin. Lab. Diagn., 58, No. 4, 17-20 (2013).

    Google Scholar 

  11. Lee, L. H, Mansoor, A., Wood, B., Nelson, H., Higa, D., an Naugler, C., “Performance of CellaVision DM96 in leukocyte classification,” J. Pathol. Inform., 4, 14 (2013).

  12. Smits, S. M. and Leyte, A., “Clinical performance evaluation of the CellaVision Image Capture System in the white blood cell differential on peripheral blood smears,” J. Clin. Pathol., 67, No. 2, 168-172 (2014).

    Article  Google Scholar 

  13. Surcouf, C., Delaune, D., Samson, T., and Foissaud ,V., “Automated cell recognition in hematology: CellaVision DM96 TM system” [Article in French], Ann. Biol. Clin. (Paris), 67, No. 4, 419-424 (2009).

  14. VanVranken, S. J., Patterson, E. S., Rudmann, S. V., and Waller, K. V., “A survey study of benefits and limitations of using CellaVision DM96 for peripheral blood differentials,” Clin. Lab. Sci., 27, No. 1, 32-39 (2014).

    Google Scholar 

  15. Yamamoto, T., Tabe, Y., Ishii, K., Itoh, S., Maeno, I., Matsumoto, K., Horii, T., Miida, T., and Ohsaka, A., “Performance evaluation of the CellaVision DM96 system in WBC differentials” [Article in Japanese], Rinsho. Byori., 58, No. 9, 884-890 (2010).

  16. CLSI H20-A Reference Leukocyte Differential Count (Proportional) and Evaluation of Instrumental Methods: Approved Standard, Vol. 12, No. 1 (1992).

  17. CLSI H20-A2 Reference Leukocyte (WBC) Differential Count (Proportional) and Evaluation of Instrumental Methods: Approved Standard, Vol. 27, No. 4 (2007).

  18. Sosnin, D. Yu., Falkov, B. F., and Nenasheva, O. Yu., “Evaluation of accuracy of cell identification by the Vision Hema automated blood analysis system,” Ural. Med. Zh., No. 13, 131-135, 140 (2012).

  19. Briggs, C., Longair, I., Slavik, M., Thwaite, K., Mills, R., Thavaraja, V., Foster, A., Romanin, D., and Machin, S. J., “Can automated blood film analysis replace the manual differential? An evaluation of the CellaVision DM96 automated image analysis system,” Int. J. Lab. Hematol., 31, No. 17, 48-60 (2009).

    Article  Google Scholar 

  20. Kratz, A., Bengtsson, H. I., Casey, J. E., Keefe, J. M., Beatrice, G. H., Grzybek, D. Y., Lewandrowski, K. B., and van Cott, E. M., “Performance evaluation of the CellaVision DM96 system: WBC differentials by automated digital image analysis supported by an artificial neural network,” Am. J. Clin. Pathol., 124, No. 5, 770-781 (2005).

  21. Yu, H., Ok, C. Y., Hesse, A., Nordell, P., Connor, D., Sjostedt, E., Pechet, L., and Snyder, L.M., “Evaluation of an automated digital imaging system, Next slide Digital Review Network, for examination of peripheral blood smears,” Arch. Pathol. Lab. Med., 136, No. 6, 660-667 (2012).

    Article  Google Scholar 

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Authors and Affiliations

  1. E.A.Vagner Perm State Medical University, Perm, Russia

    D. Yu. Sosnin

  2. OOO West Medica, Perm, Russia

    L. S. Onyanova

  3. Perm National Research Polytechnical University, Perm, Russia

    O. G. Kubarev & E. V. Kozonogova

Authors
  1. D. Yu. Sosnin
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  2. L. S. Onyanova
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  3. O. G. Kubarev
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  4. E. V. Kozonogova
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Correspondence to D. Yu. Sosnin.

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Translated from Meditsinskaya Tekhnika, Vol. 52, No. 1, Jan.-Feb., 2018, pp. 23-27.

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Sosnin, D.Y., Onyanova, L.S., Kubarev, O.G. et al. Evaluation of Efficacy of White Blood Cell Identification in Peripheral Blood by Automated Scanning of Stained Blood Smear Images with Variable Magnification. Biomed Eng 52, 31–36 (2018). https://doi.org/10.1007/s10527-018-9776-1

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  • DOI: https://doi.org/10.1007/s10527-018-9776-1

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