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使用自动显微镜检查外周血涂片;评估Diffmaster Octavia和Cellavision DM96。

Examination of peripheral blood films using automated microscopy; evaluation of Diffmaster Octavia and Cellavision DM96.

作者信息

Ceelie H, Dinkelaar R B, van Gelder W

机构信息

Department of Clinical Chemistry, Albert Schweitzer Ziekenhuis, Dordrecht, The Netherlands.

出版信息

J Clin Pathol. 2007 Jan;60(1):72-9. doi: 10.1136/jcp.2005.035402. Epub 2006 May 12.

DOI:10.1136/jcp.2005.035402
PMID:16698955
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC1860603/
Abstract

BACKGROUND

Differential counting of peripheral blood cells is an important diagnostic tool. Yet, this technique requires highly trained staff, is labour intensive and has limited statistical reliability. A recent development in this field was the introduction of automated peripheral blood differential counting systems. These computerised systems provide an automated morphological analysis of peripheral blood films, including a preclassification of both red and white cells (RBCs and WBCs, respectively).

AIMS

To investigate the ability of two automated microscopy systems to examine peripheral blood smears.

METHODS

Two automated microscopy systems, the Cellavision Diffmaster Octavia (Octavia) and Cellavision DM96 (DM96), were evaluated.

RESULTS

The overall preclassification accuracy values for the Octavia and the DM96 systems were 87% and 92%, respectively. Evaluation of accuracy (WBC analysis) showed good correlation for both automated systems when compared with manual differentiation. Total analysis time (including post classification) was 5.4 min/slide for the Octavia and 3.2 min/slide for the DM96 (100 WBC/slide) system. The DM96 required even less time than manual differentiation by an experienced biomedical scientist.

CONCLUSIONS

The Octavia and the DM96 are automated cell analysis systems capable of morphological classification of RBCs and WBCs in peripheral blood smears. Classification accuracy depends on the type of pathological changes in the blood sample. Both systems operate most effectively in the analysis of non-pathological blood samples.

摘要

背景

外周血细胞分类计数是一项重要的诊断工具。然而,这项技术需要训练有素的工作人员,劳动强度大且统计可靠性有限。该领域最近的一项进展是引入了自动外周血分类计数系统。这些计算机化系统可对外周血涂片进行自动形态学分析,包括对红细胞和白细胞(分别为RBC和WBC)进行预分类。

目的

研究两种自动显微镜系统检查外周血涂片的能力。

方法

对两种自动显微镜系统,即Cellavision Diffmaster Octavia(Octavia)和Cellavision DM96(DM96)进行评估。

结果

Octavia和DM96系统的总体预分类准确率分别为87%和92%。准确性评估(白细胞分析)显示,与手工分类相比,两种自动系统的相关性良好。Octavia系统的总分析时间(包括分类后)为每张涂片5.4分钟,DM96(每涂片100个白细胞)系统为每张涂片3.2分钟。DM96所需时间甚至比经验丰富的生物医学科学家进行手工分类所需时间还要少。

结论

Octavia和DM96是能够对外周血涂片红细胞和白细胞进行形态学分类的自动细胞分析系统。分类准确性取决于血样中病理变化的类型。两种系统在分析非病理血样时效果最佳。

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本文引用的文献

1
Performance evaluation of the CellaVision DM96 system: WBC differentials by automated digital image analysis supported by an artificial neural network.CellaVision DM96系统的性能评估:通过人工神经网络支持的自动数字图像分析进行白细胞分类计数
Am J Clin Pathol. 2005 Nov;124(5):770-81. doi: 10.1309/XMB9-K0J4-1LHL-ATAY.
2
Differential counting of blood leukocytes using automated microscopy and a decision support system based on artificial neural networks--evaluation of DiffMaster Octavia.使用自动显微镜和基于人工神经网络的决策支持系统对血液白细胞进行分类计数——DiffMaster Octavia的评估
Clin Lab Haematol. 2003 Jun;25(3):139-47. doi: 10.1046/j.1365-2257.2003.00516.x.
3
Automated image processing. Past, present, and future of blood cell morphology identification.自动化图像处理。血细胞形态识别的过去、现在与未来。
Clin Lab Med. 2002 Mar;22(1):299-315, viii. doi: 10.1016/s0272-2712(03)00076-3.
4
The analysis of cell images.细胞图像分析。
Ann N Y Acad Sci. 1966 Jan 31;128(3):1035-53. doi: 10.1111/j.1749-6632.1965.tb11715.x.
5
Imprecision of ratio-derived differential leukocyte counts.
Blood Cells. 1985;11(2):311-4, 315.
6
Statistical methods for assessing agreement between two methods of clinical measurement.评估两种临床测量方法之间一致性的统计方法。
Lancet. 1986 Feb 8;1(8476):307-10.
7
Automated differential white cell counts: a critical appraisal.
Baillieres Clin Haematol. 1990 Oct;3(4):851-69. doi: 10.1016/s0950-3536(05)80138-6.