Related Experiment Video
Updated: Jan 17, 2026
Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
Published on: March 22, 2022
Performance Evaluation of the Scopio Labs X100HT Digital Morphology Analyzer and Abnormal Cell Detection in
Vincenzo De Iuliis1, Sofia Chiatamone Ranieri1
1Department of Clinical Pathology, G. Mazzini Civil Hospital, Teramo, Italy.
Introduction:
The Scopio Labs X100HT digital morphology system has been designed to automate the classification of white blood cells (WBCs) and identify pathological elements such as blasts, immature myeloid cells, and nucleated red blood cells (NRBCs). This study evaluates its diagnostic performance against manual microscopy, the current gold standard for hematologic morphology.
Methods:
A total of 400 peripheral blood smear samples from the routine workload of the Clinical Pathology of "G. Mazzini Hospital" in Teramo were analyzed: 40 from healthy individuals and 360 from patients selected according to internal criteria aligned with the International Consensus Group for Hematology guidelines. Each sample was evaluated using both manual microscopy and the Scopio Labs X100HT Full Field PBS system, in pre-classification and after expert review, by two experienced pathologists. The digital system pre-classified results were compared with manual counts and expert post-classification.
Results:
The Scopio Labs X100HT system showed a strong correlation with manual microscopy for neutrophils and lymphocytes (r = 0.93), and a moderate correlation for eosinophils (r = 0.80). Agreement between digital pre-classification and expert post-classification was high across all WBC categories (r ranging from 0.84 to 0.98). Receiver operating characteristic (ROC) analysis demonstrated moderate diagnostic accuracy for detecting blasts (AUC = 0.72), immature myeloid cells (AUC = 0.79), and NRBCs (AUC = 0.71), which improved with expert review.
Conclusion:
The Scopio Labs X100HT Full Field PBS system demonstrated reliable performance in WBC differential analysis and identification of abnormal cells. Expert post-classification enhances diagnostic accuracy, supporting its use in clinical workflows alongside traditional hematology analyzers.
