Performance of digital morphology analyzer Vision Pro on white blood cell differentials
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Sumi Yoon
, Mikyoung Park
, Seung Wan Kim
, Hee-Won Moon
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.
Funding source: Konkuk University
Acknowledgments
This work was supported by Konkuk University Medical Center Research Grant 2020.
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Research funding: This work was supported by Konkuk University Medical Center Research Grant 2020.
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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.
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Competing interests: Authors state no conflict of interest.
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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
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
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- NT-proBNP levels in preeclampsia, intrauterine growth restriction as well as in the prediction on an imminent delivery
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- Congress Abstract
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Articles in the same Issue
- Frontmatter
- Editorial
- COVID-19: which lessons have we learned?
- Review
- Global FT4 immunoassay standardization: an expert opinion review
- Mini Review
- Mechanism of bilirubin elimination in urine: insights and prospects for neonatal jaundice
- Opinion Paper
- Laboratory medicine in the COVID-19 era: six lessons for the future
- EFLM Paper
- How to meet ISO15189:2012 pre-analytical requirements in clinical laboratories? A consensus document by the EFLM WG-PRE
- General Clinical Chemistry and Laboratory Medicine
- External quality assessment of M-protein diagnostics: a realistic impression of the accuracy and precision of M-protein quantification
- Error simulation modeling to assess the effects of bias and precision on bilirubin measurements used to screen for neonatal hyperbilirubinemia
- NT-proBNP levels in preeclampsia, intrauterine growth restriction as well as in the prediction on an imminent delivery
- Serum N-glycan fingerprint nomogram predicts liver fibrosis: a multicenter study
- Hematology and Coagulation
- Performance of digital morphology analyzer Vision Pro on white blood cell differentials
- Cardiovascular Diseases
- Prognostic implication of elevated cardiac troponin I in patients visiting emergency department without diagnosis of coronary artery disease
- Relationships between renal function variations and relative changes in cardiac troponin T concentrations based on quantile generalized additive models (qgam)
- Diabetes
- Association of hemoglobin H (HbH) disease with hemoglobin A1c and glycated albumin in diabetic and non-diabetic patients
- Comparative study of i-SENS glucometers in neonates using capillary blood samples
- Infectious Diseases
- Evaluation of four commercial, fully automated SARS-CoV-2 antibody tests suggests a revision of the Siemens SARS-CoV-2 IgG assay
- Vitamin-D levels and intensive care unit outcomes of a cohort of critically ill COVID-19 patients
- Does mid-regional pro-adrenomedullin (MR-proADM) improve the sequential organ failure assessment-score (SOFA score) for mortality-prediction in patients with acute infections? Results of a prospective observational study
- Letters to the Editors
- Global FT4 immunoassay standardization. Response to: Kratzsch J et al. Global FT4 immunoassay standardization: an expert opinion review
- Free-thyroxine standardization: waiting for Godot while well serving our patients today
- Prevalence of SARS-CoV-2 antibodies in health care personnel of two acute care hospitals in Linz, Austria
- Pediatric evaluation of clinical specificity and sensitivity of SARS-CoV-2 IgG and IgM serology assays
- Prevention and control of COVID-19 in the penitentiary of Florence
- Pooling for SARS-CoV-2-testing: comparison of three commercially available RT-qPCR kits in an experimental approach
- Very high SARS-CoV-2 load at the emergency department presentation strongly predicts the risk of admission to the intensive care unit and death
- Next-generation sequencing and RT-PCR to identify a 32-day SARS-CoV-2 carrier
- 25-Hydroxyvitamin D concentrations in COVID-19 patients hospitalized in intensive care unit during the first wave and the second wave of the pandemic
- Laboratory findings in a child with SARS-CoV-2 (COVID-19) multisystem inflammatory syndrome
- Discrepant cardiac troponin results in a young woman
- Response to: towards the rational utilization of SARS-CoV-2 serological tests in clinical practice
- Congress Abstract
- 12th National Scientific Congress SPML,29–31 October 2020, Online, Portugal