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Multicentric evaluation of the variability of digital morphology performances also respect to the reference methods by optical microscopy

  • Italian Working Group on Diagnostic Hematology of the Italian Society of Clinical Chemistry and Clinical Molecular Biology (WGDH-SIBioC)

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Despite the important diagnostic role of peripheral blood morphology, cell classification is subjective. Automated image-processing systems (AIS) provide more accurate and objective morphological evaluation. The aims of this multicenter study were the evaluation of the intra and inter-laboratory variation between different AIS in cell pre-classification and after reclassification, compared with manual optical microscopy, the reference method. Methods: Six peripheral blood samples were included in this study, for each sample, 70 May-Grunwald and Giemsa stained PB smears were prepared from each specimen and 10 slides were delivered to the seven laboratories involved. Smears were processed by both optical microscopy (OM) and AIS. In addition, the assessment times of both methods were recorded. Results: Within-laboratory Reproducibility ranged between 4.76% and 153.78%; between-laboratory Precision ranged between 2.10% and 82.2%, while Total Imprecision ranged between 5.21% and 20.60%. The relative Bland Altman bias ranged between −0.01% and 20.60%. The mean of assessment times were 326 ± 110 s and 191 ± 68 s for AIS post reclassification and OM, respectively. Conclusions: AIS can be helpful when the number of cell counted are low and can give advantages in terms of efficiency, objectivity and time saving in the morphological analysis of blood cells. They can also help in the interpretation of some morphological features and can serve as learning and investigation tools.

Original languageEnglish
Pages (from-to)1040-1049
Number of pages10
JournalInternational Journal of Laboratory Hematology
Volume44
Issue number6
DOIs
Publication statusPublished - Dec 2022

Keywords

  • automated image-processing systems
  • between-laboratory precision
  • digital morphology
  • optical microscopy
  • within-laboratory reproducibility

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