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

Abstract

IntroductionThe 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.MethodsThe 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.ResultsThe 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.ConclusionThe 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.

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

“…In this context, we have demonstrated the MC‐80's commendable performance in accurately pre‐classifying blasts in patients, exhibiting high sensitivity (92.0%). Our results are in line with several previously reported studies 11,14 . A notable advancement of the MC‐80 is its application of the deep convolutional neural network (CNN) model, which represents an improvement over previous technologies.…”

“…10 As a DM analyzer launched in China, the MC-80 has exhibited excellent performance in leukocyte morphology analysis, as indicated by several independent studies. 11,12 There is a growing trend in comparing the performance of different brands of DM analyzers, such as CellaVision DC-1 versus Sysmex DI-60, 13 Mindray MC-80 versus Cel-laVision DM 9600, 14 and Mindray MC-100i versus Sysmex DI-60. 15 CellaVision has developed a series of automated morphological analysis systems, including the DM96, DM1200, and DM 9600.…”

Enhancing thalassemia gene carrier identification in non‐anemic populations using artificial intelligence erythrocyte morphology analysis and machine learning

“…This could increase work efficiency in blood cell morphology observing. 21,22 This study shows significant differences in erythrocyte morphology between TT and control groups. The percentages of many abnormal erythrocytes in the TT group were significantly higher than those in the control group.…”

Performance Assessment of Sysmex DI-60: Is Digital Morphology Analyzer Reliable for White Blood Cell Differentials in Body Fluids?

“…The application of digital morphology (DM) analyzers has been increasing in the clinical practice for the peripheral blood smear (PBS) examination [ 1 , 2 , 3 , 4 ]. Numerous studies have reported that DM analyzers may show reliable performances for white blood cell (WBC) counts and differentials after verification [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. DM analyzers are also expected to reduce the workloads and turnaround times (TAT) of PBS examinations in clinical laboratories [ 5 , 9 , 10 , 11 , 13 ].…”