Automated differential white cell counts: a critical appraisal

Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases

Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence

INTRODUCTION

The nucleated-cell differential count on the bone marrow aspirate smears is required for the clinical diagnosis of hematological malignancy. Manual bone marrow differential count is time consuming and lacks consistency. In this study, a novel artificial intelligence (AI)-based system was developed to perform cell automatic classification of bone marrow cells and determine its potential clinical applications.

MATERIALS AND METHODS

Bone marrow aspirate smears were collected from the Xinqiao Hospital of Army Medical University. First, an automated analysis system (Morphogo) scanned and generated whole digital images of bone marrow smears. Then, the nucleated marrow cells in the selected areas of the smears at a magnification of ×1,000 were analyzed by the software utilizing an AI-based platform. The cell classification results were further reviewed and confirmed independently by 2 experienced pathologists. The automatic cell classification performance of the system was evaluated using 3 categories: accuracy, sensitivity, and specificity. Correlation coefficients and linear regression equations between automatic cell classification by the AI-based system and concurrent manual differential count were calculated.

RESULTS

In 230 cases, the classification accuracy was above 85.7% for hematopoietic lineage cells. Averages of sensitivity and specificity of the system were found to be 69.4 and 97.2%, respectively. The differential cell percentage of the automated count based on 200-500 cell counts was correlated with differential cell percentage provided by the pathologists for granulocytes, erythrocytes, and lymphocytes (r ≥ 0.762, p < 0.001).

DISCUSSION/CONCLUSION

This pilot study confirmed that the Morphogo system is a reliable tool for automatic bone marrow cell differential count analysis and has potential for clinical applications. Current ongoing large-scale multicenter validation studies will provide more information to further confirm the clinical utility of the system.

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

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.

Comparison of manual and automated leukocyte counts for determination of the absolute neutrophil count: application to a pediatric oncology clinic

BACKGROUND

Determination of the absolute neutrophil count (ANC) is a critical test prior to initiation of chemotherapy and is a standard component of cancer therapy protocols. Automated determination of this parameter potentially shortens the turnaround time necessary between specimen phlebotomy and chemotherapy infusion in an outpatient setting. However, there are certain factors that can lead to spuriously elevated or lowered ANCs, possibly leading to inappropriate dosage. We therefore compared ANC results in a series of samples in which both automated and manual results were available.

PROCEDURE

Sets of 111 specimens, tested over a 1-month period, had matched automated and manual ANC results available for initial retrospective analysis. An additional set of 35 specimens with ANCs of <1.5 x 10(9)/L were subsequently analyzed in a similar fashion. Automated ANC results were obtained with a Cell-Dyn 3500 (Abbott Diagnostics, Santa Clara, CA, USA) automated hematology analyzer, and manual ANC results were obtained using 100 cell differentials performed by 1 of 13 medical technologists. Results were tabulated and analyzed using standard linear regression and scatter plot analyses.

RESULTS

Of the initial 111 specimens, automated ANC values ranged from 0.16-14.2 x 10(9)/L (median=2.6 x 10(9)/L), as compared with 0.24-13.9 x 10(9)/L (median 3.0 x 10(9)/L) for manual ANC values (R(2) = 0.99; SE=0.49). Differences between the ANC values ranged from -55 to +33% (SD=14%) of the manual value. Of the second set of 35 specimens, regression analysis yielded an R(2) value of 0.92, with a SE of 0.11. Both data sets yielded acceptable degrees of variation on scatter plot analyses.

CONCLUSIONS

Automated ANC values appear adequate for determining suitability for chemotherapy and lessen the turnaround time between specimen phlebotomy and result verification.