BACKGROUND: This study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC-80 digital morphology analyzer.
METHODS: Digital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT-M1) and estimate factor (PLT-M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT-I and PLT-O), digital morphology analyzer (PLT-M1 and PLT-M2), and flow cytometry (PLT-IRM) were compared.
RESULTS: There was no significant deviation observed before and after verification for both PLT-M1 and PLT-M2 across the analysis range (average bias: -0.845/-0.682, 95% limit of agreement (LOA): -28.675-26.985/-29.420-28.056). When platelet alarms appeared, PLT-M1/PLT-M2 showed the strongest correlation with PLT-IRM than PLT-I with PLT-IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT-M1/PLT-M2 and PLT-IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT-M1 and PLT-M2 is related to the number of RBCs. Compared with PLT-I, PLT-M1/PLT-M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low-value PLT.
CONCLUSIONS: The novel platelet count estimation on the MC-80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.

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.

BACKGROUND: Few studies have evaluated digital morphology (DM) analyzers on body fluids (BF). We evaluated the performance of a DM analyzer, Sysmex DI-60 (Sysmex, Kobe, Japan) for white blood cell (WBC) differentials in BF samples.
METHODS: In five BF samples (two pleural fluids and three ascites) containing a single, dominant cell type (>80%, neutrophils, lymphocytes, macrophages, abnormal lymphocytes, and malignant cells in each sample), we evaluated the precision of the DI-60 and compared the WBC differentials and turnaround times (TAT) between DI-60 and manual counting.
RESULTS: The precision of the DI-60 pre-classification and verification was excellent (%CV, 0.01-3.16%). After verification, the DI-60 showed high sensitivity, specificity, and efficiency (ranges: 90.8-98.1%, 96.8-97.9%, and 92.5-98.0%, respectively) for the dominant cell types in neutrophil- and lymphocyte-dominant samples. For all samples, the DI-60 and manual counting showed high correlations for major cell types (neutrophils, lymphocytes, macrophages, and others, r = 0.72 to 0.94) after verification. The agreement between the pre-classification and verification of the DI-60 was strong in the neutrophil-dominant sample (κ = 0.81). The DI-60 showed a significantly longer TAT (min: s) than manual counting for all samples (median TAT/slide: 6:28 vs. 1:53, p < 0.0001), with remarkable differences in abnormal lymphocyte- and malignant cell-dominant samples (21:05 vs. 2:06; 12:34 vs. 2:25).
CONCLUSIONS: The DI-60 may provide reliable data in neutrophil- and lymphocyte-dominant BF samples. However, it may require longer times and higher workloads for WBC differentials especially in BF samples containing atypical cells. Further improvement would be needed before applying DM analyzers for routine clinical practice in BF analysis.

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.

Morphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood.
A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded.
For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI.
AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.

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.
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.
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).
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.