Performance of digital morphology analyzer Vision Pro on white blood cell differentials

Sumi Yoon; Mina Hur; Mikyoung Park; Hanah Kim; Seung Wan Kim; Tae-Hwan Lee; Minjeong Nam; Hee-Won Moon; Yeo-Min Yun

doi:10.1515/cclm-2020-1701

Article

Performance of digital morphology analyzer Vision Pro on white blood cell differentials

Sumi Yoon , Mina Hur , Mikyoung Park , Hanah Kim , Seung Wan Kim , Tae-Hwan Lee , Minjeong Nam , Hee-Won Moon and Yeo-Min Yun

Published/Copyright: January 20, 2021

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From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 59 Issue 6

Abstract

Objectives

Vision Pro (West Medica, Perchtoldsdorf, Austria) is a recently developed digital morphology analyzer. We evaluated the performance of Vision Pro on white blood cell (WBC) differentials.

Methods

In a total of 200 peripheral blood smear samples (100 normal and 100 abnormal samples), WBC preclassification and reclassification by Vision Pro were evaluated and compared with manual WBC count, according to the Clinical and Laboratory Standards Institute guidelines (H20-A2).

Results

The overall sensitivity was high for normal WBCs and nRBCs (80.1–98.0%). The overall specificity and overall efficiency were high for all cell classes (98.1–100.0% and 97.7–99.9%, respectively). The absolute values of mean differences between Vision Pro and manual count ranged from 0.01 to 1.31. In leukopenic samples, those values ranged from 0.09 to 2.01. For normal WBCs, Vision Pro preclassification and manual count showed moderate or high correlations (r=0.52–0.88) except for basophils (r=0.34); after reclassification, the correlation between Vision Pro and manual count was improved (r=0.36–0.90).

Conclusions

This is the first study that evaluated the performance of Vision Pro on WBC differentials. Vision Pro showed reliable analytical performance on WBC differentials with improvement after reclassification. Vision Pro could help improve laboratory workflow.

Keywords: comparison; digital morphology; manual count; performance; Vision Pro; white blood cell differentials

Corresponding author: Hanah Kim, MD, PhD, Department of Laboratory Medicine, Konkuk University School of Medicine, Konkuk University Medical Center, 120-1 Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Republic of Korea, Phone: +82-2-2030-7826, E-mail: md.hkim@gmail.com.

Funding source: Konkuk University

Acknowledgments

This work was supported by Konkuk University Medical Center Research Grant 2020.

Research funding: This work was supported by Konkuk University Medical Center Research Grant 2020.
Author contributions: Yoon S. collected the samples, analyzed the data, and wrote the draft; Kim S.W. collected the samples; Park M. and Lee T.H. analyzed the data; Kim H. conceived the study, analyzed the data, and finalized the draft; Hur M. conceived the study and finalized the draft; Nam M., Moon H.W., and Yun Y.M. discussed the data and reviewed the manuscript. All authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Competing interests: Authors state no conflict of interest.
Ethical approval: This study protocol was approved by the Institution Review Board of KUMC (KUH1200091), before collecting the first sample from the first patient.

References

1. Clinical and Laboratory Standards Institute (CLSI). Reference leukocytes (WBC) differential count (proportional) and evaluation of instrumental methods: approval standard, 2nd ed. CLSI Document H20-A2. Wayne, PA: CLSI; 2007.Search in Google Scholar

2. Da Costa, L. Digital image analysis of blood cells. Clin Lab Med 2015;35:105–22. https://doi.org/10.1016/j.cll.2014.10.005.Search in Google Scholar

3. Briggs, C, Longair, I, Slavik, M, Thwaite, K, Mills, R, Thavaraja, V, et al.. Can automated blood film analysis replace the manual differential? An evaluation of the CellaVision DM96 automated image analysis system. Int J Lab Hematol 2009;31:48–60. https://doi.org/10.1111/j.1751-553x.2007.01002.x.Search in Google Scholar

4. Simson, E, Gascon-Lema, MG, Brown, DL. Performance of automated slide makers and stainers in a working laboratory environment – routine operation and quality control. Int J Lab Hematol 2010;32:e64-76. https://doi.org/10.1111/j.1751-553x.2009.01141.x.Search in Google Scholar

5. Hur, M, Cho, JH, Kim, H, Hong, MH, Moon, HW, Yun, YM, et al.. Optimization of laboratory workflow in clinical hematology laboratory with reduced manual slide review: comparison between Sysmex XE‐2100 and ABX Pentra DX120. Int J Lab Hematol 2011;33:434–40. https://doi.org/10.1111/j.1751-553x.2011.01306.x.Search in Google Scholar

6. Kratz, A, Lee, SH, Zini, G, Riedl, J, Hur, M, Machin, S. Digital morphology analyzers in hematology: ICSH review and recommendations. Int J Lab Hematol 2019;41:437–47. https://doi.org/10.1111/ijlh.13042.Search in Google Scholar

7. Tatsumi, N, Pierre, RV. Automated image processing: past, present, and future of blood cell morphology identification. Clin Lab Med 2002;22:299–315. https://doi.org/10.1016/s0272-2712(03)00076-3.Search in Google Scholar

8. Briggs, C, Culp, N, Davis, B, D’onofrio, G, Zini, G, et al., International Council for Standardization in Haematology, Writing Group. ICSH guidelines for the evaluation of blood cell analysers including those used for differential leucocyte and reticulocyte counting. Int J Lab Hematol 2014;36:613–27. https://doi.org/10.1111/ijlh.12201.Search in Google Scholar

9. Kratz, A, Bengtsson, HI, Casey, JE, Keefe, JM, Beatrice, GH, Grzybek, DY, et al.. Performance evaluation of the CellaVision DM96 system: WBC differentials by automated digital image analysis supported by an artificial neural network. Am J Clin Pathol 2005;124:770–81. https://doi.org/10.1309/xmb9k0j41lhlatay.Search in Google Scholar

10. Cornet, E, Perol, JP, Troussard, X. Performance evaluation and relevance of the CellaVision DM96 system in routine analysis and in patients with malignant hematological diseases. Int J Lab Hematol 2008;30:536–42. https://doi.org/10.1111/j.1751-553X.2007.00996.x.Search in Google Scholar

11. Lee, LH, Mansoor, A, Wood, B, Nelson, H, Higa, D, Naugler, C. Performance of CellaVision DM96 in leukocyte classification. J Pathol Inf 2013;4:14.10.4103/2153-3539.114205Search in Google Scholar PubMed PubMed Central

12. Tabe, Y, Yamamoto, T, Maenou, I, Nakai, R, Idei, M, Horii, T, et al.. Performance evaluation of the digital cell imaging analyzer DI-60 integrated into the fully automated Sysmex XN hematology analyzer system. Clin Chem Lab Med 2015;53:281–9. https://doi.org/10.1515/cclm-2014-0445.Search in Google Scholar

13. Kim, HN, Hur, M, Kim, H, Kim, SW, Moon, HW, Yun, YM. Performance of automated digital cell imaging analyzer Sysmex DI-60. Clin Chem Lab Med 2018;56:94–102. https://doi.org/10.1515/cclm-2018-0539.Search in Google Scholar

14. Park, SJ, Yoon, J, Kwon, JA, Yoon, SY. Evaluation of the CellaVision advanced RBC application for detecting red blood cell morphological abnormalities. Ann Lab Med 2021;41:44–50. https://doi.org/10.3343/alm.2021.41.1.44.Search in Google Scholar

15. Leung, E, Johnston, A, Olsen, B, Chang, H, Martin, T, Wozniak, M, et al.. Laboratory practices for manual blood film review: results of an IQMH patterns of practice survey. Int J Lab Hematol 2020:1–7.10.1111/ijlh.13343Search in Google Scholar PubMed

16. Sosnin, DY, Onjanova, LS, Falkov, BF, Kubarev, OG, Pozdin, NV. Automated reticulocyte counting in peripheral blood smears. Biomed Eng 2017;51:249–53. https://doi.org/10.1007/s10527-017-9724-5.Search in Google Scholar

17. West, Medica. Digital microscopy automation, Artificial intelligence ∙ Clinical applications, Hematology, Digital morphology of blood cells. Available from: http://wm-vision.com/img/PDF/Vision_Hema_Rev_1.0_10.2019_brochure_en_LQ.pdf [Accessed Oct 2019].Search in Google Scholar

18. Mukaka, MM. A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 2012;24:69–71.Search in Google Scholar

19. Kratz, A, Lee, SH, Zini, G, Hur, M, Machin, S. Rebuttal of a paper submitted by Hans-Inge Bengtsson. Int J Lab Hematol 2020 Aug 4. https://doi.org/10.1111/ijlh.13279 [Epub ahead of print].Search in Google Scholar

20. Riedl, JA, Stouten, K, Ceelie, H, Boonstra, J, Levin, MD, van Gelder, W. Interlaboratory reproducibility of blood morphology using the digital microscope. J Lab Autom 2015;20:670–5. https://doi.org/10.1177/2211068215584278.Search in Google Scholar

21. Yu, H, Ok, CY, Hesse, A, Nordell, P, Connor, D, Sjostedt, E, et al.. Evaluation of an automated digital imaging system, Next slide Digital Review Network, for examination of peripheral blood smears. Arch Pathol Lab Med 2012;136:660–7. https://doi.org/10.5858/arpa.2011-0285-oa.Search in Google Scholar

22. Kim, HN, Hur, M, Kim, H, Park, M, Kim, SW, Moon, HW, et al.. Comparison of three staining methods in the automated digital cell imaging analyzer Sysmex DI-60. Clin Chem Lab Med 2018;56:e280–3. https://doi.org/10.1515/cclm-2018-0539.Search in Google Scholar

23. Saad Albichr, I, Sottiaux, JY, Hotton, J, De Laveleye, M, Dupret, P, Detry, G. Cross-evaluation of five slidemakers and three automated image analysis systems: the pitfalls of automation? Int J Lab Hematol 2020 Jun 15. https://doi.org/10.1111/ijlh.13264 [Epub ahead of print].Search in Google Scholar

24. Rosetti, M, Massari, E, Poletti, G, Dorizzi, RM. Could the UKNEQAS program “Manual Differential Blood Count” be performed by the use of an automated digital morphology analyzer (Sysmex DI-60)? A feasibility study. Clin Chem Lab Med 2021;59:e161–4. https://doi.org/10.1515/cclm-2020-0627.Search in Google Scholar

Received: 2020-11-11

Accepted: 2021-01-08

Published Online: 2021-01-20

Published in Print: 2021-05-26

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Articles in the same Issue

https://doi.org/10.1515/cclm-2020-1701

Keywords for this article

comparison; digital morphology; manual count; performance; Vision Pro; white blood cell differentials