The use of CellaVision competency software for external quality assessment and continuing professional development

Horiuchi, Yuki; Tabe, Yoko; Idei, Mayumi; Bengtsson, Hans-Inge; Ishii, Kiyoshi; Horii, Takashi; Miyake, Kazunori; Satoh, Nobuhiko; Miida, Takashi; Ohsaka, Akimichi

doi:10.1136/jcp.2011.089888

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…Finally, it has been shown that education using images captured by systems such as the DM96 allows for quicker leukocyte recognition among new trainees. [16] Future studies include the assessment of the ability of the DM96 to identify specific PBS morphologic diagnoses.…”

Section: Discussionmentioning

confidence: 99%

Performance of CellaVision DM96 in leukocyte classification

Lee

Mansoor

Wood

et al. 2013

Journal of Pathology Informatics

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Background:Leukocyte differentials are an important component of clinical care. Morphologic assessment of peripheral blood smears (PBS) may be required to accurately classify leukocytes. However, manual microscopy is labor intensive. The CellaVision DM96 is an automated system that acquires digital images of leukocytes on PBS, pre-classifies the cell type, and displays them on screen for a Technologist or Pathologist to approve or reclassify. Our study compares the results of the DM96 with manual microscopy.Methods:Three hundred and fifty-nine PBS were selected and assessed by manual microscopy with a 200 leukocyte cell count. They were then reassessed using the CellaVision DM96 with a 115 leukocyte cell count including reclassification when necessary. Correlation between the manual microscopy results and the CellaVision DM96 results was calculated for each cell type.Results:The correlation coefficients (r2) range from a high of 0.99 for blasts to a low of 0.72 for metamyelocytes.Conclusions:The correlation between the CellaVision DM96 and manual microscopy was as good or better than the previously published data. The accuracy of leukocyte classification depended on the cell type, and in general, there was lower correlation for rare cell types. However, the correlation is similar to previous studies on the correlation of manual microscopy with an established reference result. Therefore, the CellaVision DM96 is appropriate for clinical implementation.

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Section: Discussionmentioning

confidence: 99%

Performance of CellaVision DM96 in leukocyte classification

Lee

Mansoor

Wood

et al. 2013

Journal of Pathology Informatics

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“…As pointed out by many authors including VanVranken et al, Hsu et al, Horiuchi et al, important advantages of digital morphology include the ability to easily consult colleagues, reference abnormal cells, utilize archived images for education, quality assurance and competency assessment, archival, retrieval, and expert consultation from remote sites.…”

Section: Determination Of the Strengths And Weaknesses Of Available Pmentioning

confidence: 99%

Digital morphology analyzers in hematology: ICSH review and recommendations

Kratz

Lee

Zini

et al. 2019

Int J Lab Hematology

104

148

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Introduction Morphological assessment of the blood smear has been performed by conventional manual microscopy for many decades. Recently, rapid progress in digital imaging and information technology has led to the development of automated methods of digital morphological analysis of blood smears. Methods A panel of experts in laboratory hematology reviewed the literature on the use of digital imaging and other strategies for the morphological analysis of blood smears. The strengths and weaknesses of digital imaging were determined, and recommendations on improvement were proposed. Results By preclassifying cells using artificial intelligence algorithms, digital image analysis automates the blood smear review process and enables faster slide reviews. Digital image analyzers also allow remote networked laboratories to transfer images rapidly to a central laboratory for review, and facilitate a variety of essential work functions in laboratory hematology such as consultations, digital image archival, libraries, quality assurance, competency assessment, education, and training. Different instruments from several manufacturers are available, but there is a lack of standardization of staining methods, optical magnifications, color and display characteristics, hardware, software, and file formats. Conclusion In order to realize the full potential of Digital Morphology Hematology Analyzers, pre‐analytic, analytic, and postanalytic parameters should be standardized. Manufacturers of new instruments should focus on improving the accuracy of cell preclassifications, and the automated recognition and classification of pathological cell types. Cutoffs for grading morphological abnormalities should depend on clinical significance. With all current devices, a skilled morphologist remains essential for cell reclassification and diagnostic interpretation of the blood smear.

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“…When one considers the relative cost of purchase of a small analyzer capable of assessing reticulocyte counts and a CellaVision scanner, the above solution might indeed be a viable option for some; such automated analyzers have capital costs on par or greater than a comparable CellaVision system (based on CAP TODAY Hematology Analyzer Platform Product Comparisons. [27]) Other potential benefits of the CellaVision system include real-time morphologically verifiable quality-assurance capabilities,[128] as well as the capacity for centralization of morphological expertise. [1]…”

Section: Discussionmentioning

confidence: 99%

Accuracy of the CellaVision DM96 platform for reticulocyte counting

Mahé

Higa

Naugler

et al. 2014

Journal of Pathology Informatics

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Context:Many hematology laboratories have adopted semi-automated digital platforms for routine use and the evidence supporting their use is increasing.Aims:The CellaVision platforms are among the most thoroughly studied digital hematology platforms; we wished to determine the accuracy of CellaVision for reticulocyte counting.Design, Materials and Methods:We compared reticulocyte counts performed manually, using the Beckman Coulter LH750 automated analyzer and with the CellaVision DM96 platform. We analyzed the results for pair-wise correlation and bias, and precision.Statistical Analyses Used:Analyses were performed using Statistical Package for the Social Sciences software (SPSS), including Spearman's rho correlation coefficient, Friedman's two-way Analysis Of Variance (ANOVA) for comparison of distributions; bias was compared by way of mean and standard deviation.Results:The CellaVision reticulocyte counts correlated most strongly with those of the analyzer (often considered the benchmark test); the reticulocyte count distributions were noted not to be significantly different from each other across all three methods. The mean and standard deviation of bias were lowest in the comparison of CellaVision and LH750 counts.Conclusions:Our data provide additional support for the accuracy of digital hematology applications using the CellaVision DM96 platform.

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The use of CellaVision competency software for external quality assessment and continuing professional development

Abstract: The CCS proficiency testing program was useful for the quality assessment of laboratory performance, for education, and for the storage and distribution of cell images to be utilised for further standardisation and education.

Cited by 8 publications

References 8 publications

Performance of CellaVision DM96 in leukocyte classification

Performance of CellaVision DM96 in leukocyte classification

Digital morphology analyzers in hematology: ICSH review and recommendations

Accuracy of the CellaVision DM96 platform for reticulocyte counting

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