Performance of the CellaVision DC-1 digital cell imaging analyser for differential counting and morphological classification of blood cells

Leveraging computer-based simulations and immersive software technologies for enhanced student learning in laboratory medicine

Science, technology, engineering, and mathematics (STEM) students are typically taught content delivered didactically and closely aligned with the laboratory demonstration of concepts, which facilitates the development of experimental skills. Because of the volume of content delivered across multiple courses, student cognitive abilities can be affected, leading to lower student performance. In physiology and related biological sciences, educators have turned to delivering content with virtual teaching technologies, including virtual and augmented reality, simulations, and other immersive platforms. At the University of South Australia, Articulate Storyline, Unity-based simulations, and immersive software platforms have been implemented across the entire Laboratory Medicine program to assist students in learning lecture and laboratory content. The impact of these individual interventions is outlined in this article. In addition, the final year 2024 cohort is the first group who have used simulations throughout their degree program. Evidence of the benefits and impact of the scaffolded implementation of simulations and immersive software was obtained through a Likert-style questionnaire. The deployment of simulations and immersive software across the degree program has significantly enhanced student learning and engagement with the content, effectively bridging the gap between understanding lecture and laboratory content of students in the Laboratory Medicine program. We suggest that a similar approach could readily be embedded within individual courses as well as across science programs to provide the same benefits to student learning.NEW & NOTEWORTHY We cover the effective application of computer-based simulation and immersive software programs throughout a 4-year laboratory medicine degree. We demonstrate that these technologies significantly improved student learning and engagement. Such an approach is applicable to all disciplines.

Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment

Myelodysplastic syndromes represent a group of hematological neoplastic diseases caused by defective stem cells causing cytopenia and abnormal hematopoiesis. More than 30% of myelodysplastic syndrome cases develop into acute myeloid leukemia. An analysis of bone marrow samples, peripheral blood smears, multiparametric flow cytometry data, and clinical patient information is part of the current, time-consuming, and labor-intensive work up for myelodysplastic syndromes. Nowadays, clinical biomedical research has been transformed by the advent of artificial intelligence, specifically machine learning. Artificial intelligence (AI) can improve risk assessment and diagnosis, as well as boost the precision of clinical outcome prediction and illness classification. Algorithms based on artificial intelligence may be potentially helpful in discovering new needs for myelodysplastic syndrome-affected patients, choosing treatment and assessing minimal residual disease. In this review, we seek to identify the primary mechanisms and uses of artificial intelligence in myelodysplastic syndrome, pointing out its advantages and disadvantages while discussing the possible benefits of using AI pipelines in a therapeutic setting.

Real-World Application of Digital Morphology Analyzers: Practical Issues and Challenges in Clinical Laboratories

Digital morphology (DM) analyzers have advanced clinical hematology laboratories by enhancing the efficiency and precision of peripheral blood (PB) smear analysis. This review explores the real-world application of DM analyzers with their benefits and challenges by focusing on PB smear analysis and less common analyses, such as bone marrow (BM) aspirates and body fluids (BFs). DM analyzers may automate blood cell classification and assessment, reduce manual effort, and provide consistent results. However, recognizing rare and dysplastic cells remains challenging due to variable algorithmic performances, which affect diagnostic reliability. The quality of blood film as well as staining techniques significantly influence the accuracy of DM analyzers, and poor-quality samples may lead to errors. In spite of reduced inter-observer variability compared with manual counting, an expert's review is still needed for complex cases with atypical cells. DM analyzers are less effective in BM aspirates and BF examinations because of their higher complexity and inconsistent sample preparation compared with PB smears. This technology relies heavily on artificial intelligence (AI)-based pre-classifications, which require extensive, well-annotated datasets for improved accuracy. The performance variation across platforms in BM aspirates and rare-cell analysis highlights the need for AI algorithm advancements and DM analysis standardization. Future clinical practice integration will likely combine advanced digital platforms with skilled oversight to enhance diagnostic workflow in hematology laboratories. Ongoing research aims to develop robust and validated AI models for broader clinical applications and to overcome the current limitations of DM analyzers. As technology evolves, DM analyzers are set to transform laboratory efficiency and diagnostic precision in hematology.

Digital Imaging and AI Pre-classification in Hematology

A leukocyte differential of peripheral blood can be performed using digital imaging coupled with cellular pre-classification by artificial neural networks. Platelet and erythrocyte morphology can be assessed and counts estimated. Systems from a single vendor have been used in clinical practice for several years, with other vendors' systems, in a development. These systems perform comparably to traditional manual optical microscopy, however, it is important to note that they are designed and intended to be operated by a trained morphologist. These systems have several benefits including increased standardization, efficiency, and remote-review capability.

Performance evaluation of the digital morphology analyser Sysmex DI-60 for white blood cell differentials in abnormal samples

Sysmex DI-60 enumerates and classifies leukocytes. Limited research has evaluated the performance of Sysmex DI-60 in abnormal samples, and most focused on leukopenic samples. We evaluate the efficacy of DI-60 in determining white blood cell (WBC) differentials in normal and abnormal samples in different WBC count. Peripheral blood smears (n = 166) were categorised into normal control and disease groups, and further divided into moderate and severe leucocytosis, mild leucocytosis, normal, mild leukopenia, and moderate and severe leukopenia groups based on WBC count. DI-60 preclassification and verification and manual counting results were assessed using Bland-Altman and Passing-Bablok regression analyses. The Kappa test compared the concordance in the abnormal cell detection between DI-60 and manual counting. DI-60 exhibited notable overall sensitivity and specificity for all cells, except basophils. The correlation between the DI-60 preclassification and manual counting was high for segmented neutrophils, band neutrophils, lymphocytes, and blasts, and improved for all cell classes after verification. The mean difference between DI-60 and manual counting for all cell classes was significantly high in moderate and severe leucocytosis (WBC > 30.0 × 109/L) and moderate and severe leukopenia (WBC < 1.5 × 109/L) groups. For blast cells, immature granulocytes, and atypical lymphocytes, the DI-60 verification results were similar to the manual counting results. Plasma cells showed poor agreement. In conclusion, DI-60 demonstrates consistent and reliable analysis of WBC differentials within the range of 1.5-30.0 × 109. Manual counting was indispensable in examining moderate and severe leucocytosis samples, moderate and severe leukopenia samples, and in enumerating of monocytes and plasma cells.

Performance comparison of two automated digital morphology analyzers for leukocyte differential in patients with malignant hematological diseases: Mindray MC-80 and Sysmex DI-60

BACKGROUND

The MC-80 (Mindray, Shenzhen, China), a newly available artificial intelligence (AI)-based digital morphology analyzer, is the focus of this study. We aim to compare the leukocyte differential performance of the Mindray MC-80 with that of the Sysmex DI-60 and the gold standard, manual microscopy.

METHODS

A total of 100 abnormal peripheral blood (PB) smears were compared across the MC-80, DI-60, and manual microscopy. Sensitivity, specificity, predictive value, and efficiency were calculated according to the Clinical and Laboratory Standards Institute (CLSI) EP12-A2 guidelines. Comparisons were made using Bland-Altman analysis and Passing-Bablok regression analysis. Additionally, within-run imprecision was evaluated using five samples, each with varying percentages of mature leukocytes and blasts, in accordance with CLSI EP05-A3 guidelines.

RESULTS

The within-run coefficient of variation (%CV) of the MC-80 for most cell classes in the five samples was lower than that of the DI-60. Sensitivities for the MC-80 ranged from 98.2% for nucleated red blood cells (NRBC) to 28.6% for reactive lymphocytes. The DI-60's sensitivities varied between 100% for basophils and reactive lymphocytes, and 11.1% for metamyelocytes. Both analyzers demonstrated high specificity, negative predictive value, and efficiency, with over 90% for most cell classes. However, the DI-60 showed relatively lower specificity for lymphocytes (73.2%) and lower efficiency for blasts and lymphocytes (80.1% and 78.6%, respectively) compared with the MC-80. Bland-Altman analysis indicated that the absolute mean differences (%) ranged from 0.01 to 4.57 in MC-80 versus manual differential and 0.01 to 3.39 in DI-60 versus manual differential. After verification by technicians, both analyzers exhibited a very high correlation (r = 0.90-1.00) with the manual differential results in neutrophils, lymphocytes, and blasts.

CONCLUSIONS

The Mindray MC-80 demonstrated good performance for leukocyte differential in PB smears, notably exhibiting higher sensitivity for blasts identification than the DI-60.

From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film

Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.

Evaluation of the Sysmex DI-60 digital morphology analyzer on Wright-stained samples with a focus on prevalence-dependent quality indicators

BACKGROUND

This study aims to evaluate the trueness of the DI-60 Digital Cell Imaging Analyzer on Wright-stained samples with a focus on prevalence-dependent quality indicators for differential blood counts requested from non-hematology wards.

METHODS

Two hundred and ninety-nine samples were included into this performance evaluation study at the Department of Laboratory Medicine, Medical University of Vienna. The following aspects were verified: (a) the reliability of automatedly pre-classified differential counts, (b) the concordance of DI-60 counts with manual-microscopic differential counts and (c) the agreement of DI-60 and manual-microscopic results regarding clinically relevant findings.

RESULTS

82.3% of all leukocytes were correctly pre-classified. Cell categories with a low prevalence (eosinophils, basophils, progenitors/precursors) in non-hematological patients presented with a low positive predictive value (PPV), indicating a high frequency of false positives. Comparisons between visually adjusted results of the DI-60 and manual-microscopic differential counts revealed a good concordance for neutrophil and lymphocyte counts. Besides the detection of precursors/progenitors and normoblasts, no relevant systemic errors were detected. However, due to their low prevalence and technical aspects, the detection of basophilia, monocytosis or the presence of precursors/progenitors showed comparably low accuracies (error rates of 7.4%-24.1%).

CONCLUSION

The DI-60 system works well for Wright-stained samples collected in the non-hematology ward. Due to the varying prevalence of cell categories found in peripheral blood, a low PPV can be expected with automatic assignment for those cells with low prevalence (e.g., basophils, eosinophils, precursor and progenitor cells, plasma cells). If the pre-test probability of these conditions is increased, manual microscopic processing may be recommended.

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

INTRODUCTION

The manual differential count has been recognized for its disadvantages, including large interobserver variability and labor intensiveness. In this light, automated digital cell morphology analyzers have been increasingly adopted in hematology laboratories for their robustness and convenience. This study aims to evaluate the white blood cell differential performance of the Mindray MC-80, the new automated digital cell morphology analyzer.

METHODS

The cell identification performance of Mindray MC-80 was evaluated for sensitivity and specificity using pre-classification and post-classification of each cell class. The method comparison study used manual differentials as the gold standard for calculating Pearson correlation, Passing-Bablok regression, and Bland-Altman analysis. In addition, the precision study was performed and evaluated.

RESULTS

The precision was within the acceptable limit for all cell classes. Overall, the specificity of cell identification was higher than 95% for all cell classes. The sensitivity was greater for 95% for most cell classes, except for myelocytes (94.9%), metamyelocytes (90.9%), reactive lymphocytes (89.7%), and plasma cells (60%). Pre-classification and post-classification results correlated well with the manual differential results for all the cell types investigated. The regression coefficients were greater than 0.9 for most cell classes except for promyelocytes, metamyelocytes, basophils, and reactive lymphocytes.

CONCLUSION

The performance of Mindray MC-80 for white blood cell differentials is reliable and seems to be acceptable even in abnormal samples. However, the sensitivity is less than 95% for certain abnormal cell types, so the user should be aware of this limitation where such cells are suspected.

Analytical validation of white blood cell differential and platelet assessment on the Sysmex DI-60 digital morphology analyzer

INTRODUCTION

Digital morphology analyzers are increasingly replacing light microscopy in laboratory hematology practice. This study aimed to perform the analytical validation of the white blood cell (WBC) differential and of reliability of platelet assessment on Sysmex DI-60 (Kobe, Japan).

METHODS

Validation included determination of within-run and between-run precision for WBC differential according to the CLSI EP15-A3 protocol, accuracy and method comparison with light microscopy and with the automated WBC differential from the Sysmex XN-10 hematology analyzer, reliability of platelet clump detection and platelet count estimation.

RESULTS

Standard deviations of both pre- and post-classification mostly satisfied manufacturer's criteria for imprecision. Accuracy assessment revealed that only eosinophil count (1.4%) in one peripheral blood smear (PBS) remained outside the declared range (2-10%) after reclassification. Method comparison between DI-60 and light microscopy yielded Spearman's correlation coefficients from 0.37 (basophils) to 0.94 (neutrophils and lymphocytes), minor proportional difference for bands, constant difference for monocytes, both constant and proportional difference for lymphocytes and statistically significant biases for bands, lymphocytes, monocytes and basophils. Diagnostic sensitivity (Se) and specificity (Sp) of DI-60 in detecting immature/pathological cells were 88.7% (95%CI:81.1-94.0) and 83.0% (95%CI:78.7-86.7), respectively, with the area under the curve (AUC) of 0.86 (95%CI:0.82-0.89). Agreement in detection of platelet clumps was 94.8% (kappa coefficient = 0.67, 95%CI:0.53-0.80). Se and Sp of DI-60 to detect platelet clumps were 65.7% (95%CI: 47.8-80.9) and 96.9% (95%CI: 93.9-98.6), respectively, while AUC was 0.81 (95%CI: 0.76-0.86).

CONCLUSION

DI-60 provides reliable WBC differential and platelet assessment. In doubtful cases, the use of light microscopy is still mandatory.

Digital assessment of peripheral blood and bone marrow aspirate smears

The diagnosis of benign and neoplastic hematologic disorders relies on analysis of peripheral blood and bone marrow aspirate smears. As demonstrated by the widespread laboratory adoption of hematology analyzers for automated assessment of peripheral blood, digital analysis of these samples provides many significant benefits compared to relying solely on manual review. Nonetheless, analogous instruments for digital bone marrow aspirate smear assessment have yet to be clinically implemented. In this review, we first provide a historical overview detailing the implementation of hematology analyzers for digital peripheral blood assessment in the clinical laboratory, including the improvements in accuracy, scope, and throughput of current instruments over prior generations. We also describe recent research in digital peripheral blood assessment, particularly in the development of advanced machine learning models that may soon be incorporated into commercial instruments. Next, we provide an overview of recent research in digital assessment of bone marrow aspirate smears and how these approaches could soon lead to development and clinical adoption of instrumentation for automated bone marrow aspirate smear analysis. Finally, we describe the relative advantages and provide our vision for the future of digital assessment of peripheral blood and bone marrow aspirate smears, including what improvements we can soon expect in the hematology laboratory.

Performance of digital morphology analyzer CellaVision DC-1

OBJECTIVES

CellaVision DC-1 (DC-1, Sysmex, Kobe, Japan) is a newly launched digital morphology analyzer that was developed mainly for small to medium-volume laboratories. We evaluated the precision, qualitative performance, comparison of cell counts between DC-1 and manual counting, and turnaround time (TAT) of DC-1.

METHODS

Using five peripheral blood smear (PBS) slides spanning normal white blood cell (WBC) range, precision and qualitative performance of DC-1 were evaluated according to the Clinical and Laboratory Standards Institute (CLSI) EP15-A3, EP15-Ed3-IG1, and EP12-A2 guidelines. Cell counts of DC-1 and manual counting were compared according to the CLSI EP 09C-ED3 guidelines, and TAT of DC-1 was also compared with TAT of manual counting.

RESULTS

DC-1 showed excellent precision (%CV, 0.0-3.5%), high specificity (98.9-100.0%), and high negative predictive value (98.4-100.0%) in 18 cell classes (12 WBC classes and six non-WBC classes). However, DC-1 showed 0% of positive predictive value in seven cell classes (metamyelocytes, myelocytes, promyelocytes, blasts, plasma cells, nucleated red blood cells, and unidentified). The largest absolute mean differences (%) of DC-1 vs. manual counting was 2.74. Total TAT (min:s) was comparable between DC-1 (8:55) and manual counting (8:55).

CONCLUSIONS

This is the first study that comprehensively evaluated the performance of DC-1 including its TAT. DC-1 has a reliable performance that can be used in small to medium-volume laboratories for assisting PBS review. However, DC-1 may make unnecessary workload for cell verification in some cell classes.