Evaluation of a Digital Image Analyzer UIMD PBIA for Determining White Blood Cell Differential Count
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참고문헌
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초록
Background: The UIMD PBIA (ANI CO., Suwon, Korea) is a newly developed automated digital image analyzer using innovative algorithms for the analysis of peripheral blood smears. We evaluated the accuracy and throughput of UIMD PBIA for the classification of white blood cells (WBCs). Methods: A total of 29,605 cells in 242 clinical samples (192 samples with abnormal findings and 50 normal samples) were used to evaluate the classification accuracy and throughput of the UIMD PBIA. In addition, the total processing time for WBC classification by UIMD PBIA was measured to calculate the throughput. Results: UIMD PBIA revealed outstanding performance for the identification of normal samples (99.0% accuracy) and five-part differentials (neutrophil, lymphocyte, monocyte, eosinophil, basophil, 99.2% accuracy). Misclassifications frequently occurred for immature granulocytes (83.6-93.9% accuracy), blasts (93.5% accuracy), and abnormal lymphocytes (81% accuracy). The pathogenic cells were likely to be misclassified into other classes of the same lineage. The average throughput was approximately 42 slides per hour. In cases with pancytopenia, the throughput was approximately 29 slides per hour. Conclusions: The UIMD PBIA offers the most accurate results for WBC classification and the highest throughput, thereby reducing the technical workload, especially in cases with normal findings and pancytopenia. Accordingly, this study revealed the feasibility of using a digital switch in CBC analysis.
Abstract
Background: The UIMD PBIA (ANI CO., Suwon, Korea) is a newly developed automated digital image analyzer using innovative algorithms for the analysis of peripheral blood smears. We evaluated the accuracy and throughput of UIMD PBIA for the classification of white blood cells (WBCs). Methods: A total of 29,605 cells in 242 clinical samples (192 samples with abnormal findings and 50 normal samples) were used to evaluate the classification accuracy and throughput of the UIMD PBIA. In addition, the total processing time for WBC classification by UIMD PBIA was measured to calculate the throughput. Results: UIMD PBIA revealed outstanding performance for the identification of normal samples (99.0% accuracy) and five-part differentials (neutrophil, lymphocyte, monocyte, eosinophil, basophil, 99.2% accuracy). Misclassifications frequently occurred for immature granulocytes (83.6-93.9% accuracy), blasts (93.5% accuracy), and abnormal lymphocytes (81% accuracy). The pathogenic cells were likely to be misclassified into other classes of the same lineage. The average throughput was approximately 42 slides per hour. In cases with pancytopenia, the throughput was approximately 29 slides per hour. Conclusions: The UIMD PBIA offers the most accurate results for WBC classification and the highest throughput, thereby reducing the technical workload, especially in cases with normal findings and pancytopenia. Accordingly, this study revealed the feasibility of using a digital switch in CBC analysis.
참고문헌 (16)
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