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目的:评估人工智能（AI）血细胞形态分析仪（AI阅片机）对外周血白细胞检测的性能。方法:多中心研究。（1）从全国11家三级医院收集3 010份静脉血标本，用AI阅片机进行14类白细胞分析，并且将预分类结果与资深形态学专家审核后结果进行比较，以此评估AI阅片机白细胞预分类的符合率、检出率、特异度和一致性。（2）从3 010份血液标本中选取400份血液标本（预分类人工审核后含异常白细胞的标本不少于50%），由形态学专家进行人工镜检并分析人工审核后的WBC分类结果和专家显微镜镜检结果之间的相关性。（3）从3 010份血液标本中找出诊断为淋巴瘤、急性淋巴细胞白血病、急性髓系白血病、骨髓增生异常综合征和骨髓增殖性肿瘤患者的标本结果，通过比较预分类和专家审核后结果，反映AI阅片机对这5种恶性血液病中具有重要临床意义的异常白细胞的预分类的能力。WBC预分类和专家审核结果的一致性分析采用Cohen′s Kappa 检验，比对试验采用Passing-Bablok回归分析，并根据公式统计符合率、检出率、特异度、准确度。结果:（1）AI阅片机可对14类白细胞及有核红细胞进行预分类，与形态学专家审核后结果对比，白细胞总的预分类符合率达97.97%，其中正常类型白细胞预分类符合率均>96%，总的异常白细胞预分类符合率>87%。（2）AI阅片机专家审核后结果与人工镜检对比，各种白细胞类型及有核红细胞均具有较好的相关性（中性粒细胞、淋巴细胞、单核细胞、嗜酸性粒细胞、嗜碱性粒细胞、各阶段幼稚粒细胞、原始细胞、有核红细胞、恶性细胞均 r>0.90，反应性淋巴细胞 r=0.85），参考国际血液学复检专家组定义的白细胞阳性血涂片标准，AI阅片机对白细胞异常标本具有与人工镜检同等的筛查能力。（3）对5种恶性血液病中有较大意义的白细胞与形态学专家审核后结果进行对比，AI阅片机对原始细胞预分类符合率均>84%，检出率均>94%。在急性髓系白血病中，异常早幼粒细胞检出率为95.40%。 结论:AI阅片机对外周血中各类型白细胞的预分类结果与专家审核后结果的总体符合率和检出率均较高，与人工镜检结果具有良好的相关性，且为恶性血液疾病的筛查提供了高效的辅助检测手段。

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.