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Ye X, Fang L, Chen Y, Tong J, Ning X, Feng L, Xu Y, Yang D. Performance comparison of two automated digital morphology analyzers for leukocyte differential in patients with malignant hematological diseases: Mindray MC-80 and Sysmex DI-60. Int J Lab Hematol 2024; 46:457-465. [PMID: 38212663 DOI: 10.1111/ijlh.14227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 12/28/2023] [Indexed: 01/13/2024]
Abstract
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.
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Affiliation(s)
- Xianfei Ye
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou, People's Republic of China
| | - Lijuan Fang
- Hangzhou Dian Medical Laboratory Center Co., Ltd, Hangzhou, People's Republic of China
| | - Yunying Chen
- Department of Laboratory Medicine, Hangzhou Children's Hospital, Hangzhou, People's Republic of China
| | - Jixiang Tong
- Department of Hematology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Xiaoni Ning
- Hangzhou Dian Medical Laboratory Center Co., Ltd, Hangzhou, People's Republic of China
| | - Lanjun Feng
- Hangzhou Dian Medical Laboratory Center Co., Ltd, Hangzhou, People's Republic of China
| | - Yuting Xu
- Department of Hematology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Dagan Yang
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
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Jiang H, Xu W, Chen W, He J, Jiang H, Mao Z, Liu M, Li M, Liu D, Pan Y, Qu C, Qu L, Sun Z, Sun D, Wang X, Wang J, Wu W, Xing Y, Zhang S, Zhang C, Zheng L, Guan M. Performance of the digital cell morphology analyzer MC-100i in a multicenter study in tertiary hospitals in China. Clin Chim Acta 2024; 555:117801. [PMID: 38296220 DOI: 10.1016/j.cca.2024.117801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/04/2024]
Abstract
BACKGROUND This study investigated the performance of the MC-100i, a pre-commercial digital morphology analyzer utilizing a convolutional neural network algorithm, in a multicentric setting involving up to 11 tertiary hospitals in China. METHODS Blood smears were analyzed by MC-100i, verified by morphologists, and manually differentiated. The classification performance on WBCs and RBCs was evaluated by comparing the classification results using different methods. The PLT and PLT clump counting performance was also assessed. The total assay time including hands-on time was evaluated. RESULTS The agreements between pre- and post-classification were high for normal WBCs (κ > 0.96) and lower for overall abnormal WBCs (κ = 0.90). The post-classification results correlated well with manual differentials for both normal and abnormal WBCs (r > 0.93), except for basophils (r = 0.8480) and atypical lymphocytes (r = 0.8211). The clinical sensitivity and specificity of each RBC abnormality after verification were above 90 % using microscopy reviews as the reference. The PLTs counted by the MC-100i before and after verification correlated well with those measured by the PLT-O mode (r = 0.98). Moreover, PLT clumps were successfully classified by the analyzer in EDTA-dependent pseudothrombocytopenia blood samples. CONCLUSIONS The MC-100i is an accurate and reliable digital cell morphology analyzer, offering another intelligent option for hematology laboratories.
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Affiliation(s)
- Hong Jiang
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610044, China
| | - Wei Xu
- Department of Laboratory Medicine, The First Bethune Hospital of Jilin University, Jilin 130061, China
| | - Wei Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Jun He
- Department of Laboratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - Haoqin Jiang
- Department of Laboratory Medicine, Huashan Hospital Fudan University, Shanghai 200040, China
| | - Zhigang Mao
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610044, China
| | - Min Liu
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510062, China
| | - Mianyang Li
- Department of Laboratory Medicine, Chinese PLA Ceneral Hospital, Beijing 100080, China
| | - Dandan Liu
- Department of Laboratory Medicine, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - Yuling Pan
- Department of Laboratory Medicine, Chinese PLA Ceneral Hospital, Beijing 100080, China
| | - Chenxue Qu
- Department of Laboratory Medicine, Peking University First Hospital, Beijing 100034, China
| | - Linlin Qu
- Department of Laboratory Medicine, The First Bethune Hospital of Jilin University, Jilin 130061, China
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College of Hust, Wuhan 430030, China
| | - Dehua Sun
- Department of Laboratory Medicine, Nanfang Hospital, Guangzhou 516006, China
| | - Xuefeng Wang
- Department of Laboratory Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - Jianbiao Wang
- Department of Laboratory Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
| | - Wenjing Wu
- Department of Laboratory Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Ying Xing
- Department of Laboratory Medicine, Peking University First Hospital, Beijing 100034, China
| | - Shihong Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510062, China
| | - Chi Zhang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College of Hust, Wuhan 430030, China
| | - Lei Zheng
- Department of Laboratory Medicine, Nanfang Hospital, Guangzhou 516006, China.
| | - Ming Guan
- Department of Laboratory Medicine, Huashan Hospital Fudan University, Shanghai 200040, China.
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