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Home / Equine Research Bank / BMC Vet Res / Comparison of Sysmex XN-V body fluid mode and deep-learning-...
BMC veterinary research2024; 20(1); 48; doi: 10.1186/s12917-024-03884-5

Comparison of Sysmex XN-V body fluid mode and deep-learning-based quantification with manual techniques for total nucleated cell count and differential count for equine bronchoalveolar lavage samples.

Abstract: Bronchoalveolar lavage (BAL) is a diagnostic method for the assessment of the lower respiratory airway health status in horses. Differential cell count and sometimes also total nucleated cell count (TNCC) are routinely measured by time-consuming manual methods, while faster automated methods exist. The aims of this study were to compare: 1) the Sysmex XN-V body fluid (BF) mode with the manual techniques for TNCC and two-part differential into mononuclear and polymorphonuclear cells; 2) the Olympus VS200 slide scanner and software generated deep-learning-based algorithm with manual techniques for four-part differential cell count into alveolar macrophages, lymphocytes, neutrophils, and mast cells. The methods were compared in 69 clinical BAL samples. Results: Incorrect gating by the Sysmex BF mode was observed on many scattergrams, therefore all samples were reanalyzed with manually set gates. For the TNCC, a proportional and systematic bias with a correlation of r = 0.79 was seen when comparing the Sysmex BF mode with manual methods. For the two-part differential count, a mild constant and proportional bias and a very small mean difference with moderate limits of agreement with a correlation of r = 0.84 and 0.83 were seen when comparing the Sysmex BF mode with manual methods. The Sysmex BF mode classified significantly more samples as abnormal based on the TNCC and the two-part differential compared to the manual method. When comparing the Olympus VS200 deep-learning-based algorithm with manual methods for the four-part differential cell count, a very small bias in the regression analysis and a very small mean difference in the difference plot, as well as a correlation of r = 0.85 to 0.92 were observed for all four cell categories. The Olympus VS200 deep-learning-based algorithm also showed better precision than manual methods for the four-part differential cell count, especially with an increasing number of analyzed cells. Conclusions: The Sysmex XN-V BF mode can be used for TNCC and two-part differential count measurements after reanalyzing the samples with manually set gates. The Olympus VS200 deep-learning-based algorithm correlates well with the manual methods, while showing better precision and can be used for a four-part differential cell count.
© 2024. The Author(s).
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Publication Date: 2024-02-05 PubMed ID: 38317167PubMed Central: 4913592DOI: 10.1186/s12917-024-03884-5Google Scholar: Lookup
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Summary

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This study evaluates two different techniques for determining cell counts in equine bronchoalveolar lavage samples, comparing them to traditional manual methods. The researchers studied Sysmex XN-V body fluid mode and a deep-learning-based algorithm from Olympus, with the aims to improve speed and accuracy of diagnostic measures for lower respiratory airway health in horses.

Study Overview

  • The experiment involved comparing two automated methods for assessing cell counts in equine bronchoalveolar lavage (BAL) samples against traditional manual methods. BAL is a diagnostic method to assess the health of lower respiratory airways.
  • The study aimed toward increasing the speed and accuracy of the diagnostic measure by using automated methods. Manual methods are typically used but are time-consuming.
  • Two tests were performed: One comparing Sysmex XN-V body fluid mode with manual techniques for total nucleated cell count (TNCC) and two-part differential cell count, the other comparing Olympus VS200 based on a deep-learning algorithm with manual techniques for a four-part differential cell count.

Test Results

  • In the first test, a significant number of gating errors by the Sysmex device were noticed, leading to all samples being reanalyzed with manually set gates.
  • A comparison of the Sysmex analysis with manual techniques for TNCC showed a proportional and systematic bias, with a correlation of r = 0.79.
  • A similar comparison for the two-part differential count showed a mild constant and proportional bias. The Sysmex mode classified more samples as abnormal compared to manual methods.
  • In the second test, the Olympus VS200 deep-learning algorithm showed a very small bias when compared against manual methods for the four-part differential cell count. There was also a small mean difference in the difference plot.
  • The correlation between the Olympus technique and manual method was between r = 0.85 to 0.92 across all four cell categories. The algorithm demonstrated improved precision, especially with an increasing number of analyzed cells.

Conclusion

  • The Sysmex XN-V body fluid mode can be used for both TNCC and two-part differential count measurements after corrections involving reanalysis with manually set gates.
  • The Olympus deep learning algorithm performed better in terms of precision compared to manual methods and showed a good correlation with manual methods. It can hence be used for four-part differential cell count.

Cite This Article

APA
Lapsina S, Riond B, Hofmann-Lehmann R, Stirn M. (2024). Comparison of Sysmex XN-V body fluid mode and deep-learning-based quantification with manual techniques for total nucleated cell count and differential count for equine bronchoalveolar lavage samples. BMC Vet Res, 20(1), 48. https://doi.org/10.1186/s12917-024-03884-5

Publication

BMC veterinary research
ISSN: 1746-6148
NlmUniqueID: 101249759
Country: England
Language: English
Volume: 20
Issue: 1
Pages: 48

Researcher Affiliations

Lapsina, Sandra
  • Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, CH-8057, Zurich, Switzerland. sandralapsina@live.com.
Riond, Barbara
  • Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, CH-8057, Zurich, Switzerland.
Hofmann-Lehmann, Regina
  • Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, CH-8057, Zurich, Switzerland.
Stirn, Martina
  • Clinical Laboratory, Department of Clinical Diagnostics and Services, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 260, CH-8057, Zurich, Switzerland.

MeSH Terms

  • Animals
  • Horses
  • Deep Learning
  • Body Fluids
  • Cell Count / veterinary
  • Lymphocytes
  • Algorithms
  • Leukocyte Count / veterinary
  • Reproducibility of Results

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