Cross‐evaluation of five slidemakers and three automated image analysis systems: The pitfalls of automation?

Abstract

Introduction Automated slidemakers and stainers and digital microscopes are coupled with haematology analysers to achieve better efficiency and cost‐effectiveness. This study evaluates the integrated performance of slidemakers and digital microscopes commonly available on the market. Methods We compared the percentage of neutrophils for five slidemakers (two Siemens Advia Autoslides, a SysmexSP‐10 and SP‐50 and an Abbot Alinity hs) and a Horiba Hemaprep to the corresponding haematology analyser data (Siemens Advia 2120i, Sysmex XN and Abbot Alinity hq). Differential leucocyte counting (DLC) was performed on three different CellaVision digital microscopes (DM96, DM1200 and DI‐60) and manually. The quality of the smears was assessed using a CellaVision SmearChecker. Results We observed a significant positive absolute bias (P < .05) for the percentage of neutrophils with the Autoslide and Alinity hs smears on the digital microscopes, but not when DLC was performed manually. The SP‐10 and SP‐50 showed no bias regardless of the DLC method. No bias was observed for the Hemaprep smears. All the smears had an acceptable monolayer quality, stain intensity and colour. All smears, except those from Sp‐10, were of an acceptable length. Conclusion Users should be aware of a potential lack of accuracy that can be encountered when using some slidemakers and digital microscopes. All laboratories should validate or verify the differential counts from slidemakers and digital microscope with automated cell differential counters. Manual count validation should only be considered if a significant proportion of clinically relevant abnormal cells are present. Otherwise, haematology analyser results should be favoured.

Retracted: White blood cell differentials performance of a new automated digital cell morphology analyzer: Mindray MC‐80

“…For cell identification performance of MC-80, our study revealed good agreement between pre-and postclassification for most cell classes with excellent specificity at above 95% for all cell classes, which is comparable with various image analyzer models as reported by several studies. 6,7,[9][10][11]19,21 Interestingly, poor performance for some cell classes, for example, band neutrophils and immature granulocytes, which were consistently described in many reports regardless of the analyzer model or statistics, are also found in this study. 6,7,[9][10][11]21 The excellent sensitivity is found in most cell classes (more than 95% sensitivity) but not for blast cells (86.1%), immature granulocytes (83.1%), and reactive lymphocytes (79.6%).…”

Combining imaging flow cytometry and machine learning for high-throughput schistocyte quantification: A SVM classifier development and external validation cohort

“… 14 Moreover, its performance depends on the quality of the smear to adequately choose the correct reading area with the proper repartition of cells. 7 , 8 , 15 , 38 , 39 …”

“…14 Moreover, its performance depends on the quality of the smear to adequately choose the correct reading area with the proper repartition of cells. 7,8,15,38,39 By combining the advantages of flow cytometry and microscopy, IFC is a highly suitable technology for RBC evaluation. Indeed, it is a high throughput technology capable of acquiring up to a thousand events per second.…”

“… 13 , 14 In addition, the accuracy of this approach is highly affected by the quality of the blood smear and its staining. 4 , 11 , 15 …”

“…13,14 In addition, the accuracy of this approach is highly affected by the quality of the blood smear and its staining. 4,11,15 Imaging flow cytometry (IFC) is an innovative tool that has the advantage of combining high-throughput technology with morphological analysis in the absence of pre-analytical procedures, which may modify cell shape. Indeed, IFC has proven its ability to identify various RBC anomalies, including sickle cells, 16 intra-erythrocyte malaria, 17 RBC storage lesions, 18,19 and spherocytes.…”