Clinical laboratory · 2025
Performance Evaluation of the Automated Cell Image Analyzer DI-60 for Leukocyte Differential Count.
СтатьяОценочное исследованиеНазвание и аннотация: Перевод в очереди
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Chang Soon Hee
Аннотация
BACKGROUND: The Sysmex DI-60 system (Sysmex, Kobe, Japan) is an automated cell image analyzer that captures and analyzes cell images. The aim of this study was to evaluate the performance of DI-60 for leukocyte differential count in comparison with manual differential count and XN-20.
METHODS: A total of 205 samples were analyzed and the agreement between DI-60 pre-classification and post-verification by medical technicians was determined. The correlation and ability to identify clinically important abnormal cells of DI-60 post-verification were then evaluated.
RESULTS: The overall agreement of DI-60 pre-classification was 84.7%. The correlation between DI-60 post-verification and manual differential counts was excellent (r2 > 0.85) for neutrophils, lymphocytes, monocytes, and eosinophils, but was very low for basophils (r2 = 0.3759). The DI-60, post-verification, was able to identify clinically important abnormal cells including blasts, promyelocytes, myelocytes, and metamyelocytes with high efficiency (80.8 - 96.3%), but not NRBCs (51.0%). Overall sensitivity was 63.7 - 88.2%, and specificity was 84.3 - 95.5%, excluding NRBCs (43.7%).
CONCLUSIONS: DI-60 showed excellent pre-classification accuracy and relatively high correlation compared with manual leukocyte differential counts. Although additional verification with a PBS review by experienced medical technicians may still be required, the DI-60 can be used as an efficient screening tool in clinical laboratories.
METHODS: A total of 205 samples were analyzed and the agreement between DI-60 pre-classification and post-verification by medical technicians was determined. The correlation and ability to identify clinically important abnormal cells of DI-60 post-verification were then evaluated.
RESULTS: The overall agreement of DI-60 pre-classification was 84.7%. The correlation between DI-60 post-verification and manual differential counts was excellent (r2 > 0.85) for neutrophils, lymphocytes, monocytes, and eosinophils, but was very low for basophils (r2 = 0.3759). The DI-60, post-verification, was able to identify clinically important abnormal cells including blasts, promyelocytes, myelocytes, and metamyelocytes with high efficiency (80.8 - 96.3%), but not NRBCs (51.0%). Overall sensitivity was 63.7 - 88.2%, and specificity was 84.3 - 95.5%, excluding NRBCs (43.7%).
CONCLUSIONS: DI-60 showed excellent pre-classification accuracy and relatively high correlation compared with manual leukocyte differential counts. Although additional verification with a PBS review by experienced medical technicians may still be required, the DI-60 can be used as an efficient screening tool in clinical laboratories.