Introduction: The nucleated-cell differential count on the bone marrow aspirate smears is required for the clinical diagnosis of hematological malignancy. Manual bone marrow differential count is time consuming and lacks consistency. In this study, a novel artificial intelligence (AI)-based system was developed to perform cell automatic classification of bone marrow cells and determine its potential clinical applications. Materials and Methods: Bone marrow aspirate smears were collected from the Xinqiao Hospital of Army Medical University. First, an automated analysis system (Morphogo) scanned and generated whole digital images of bone marrow smears. Then, the nucleated marrow cells in the selected areas of the smears at a magnification of ×1,000 were analyzed by the software utilizing an AI-based platform. The cell classification results were further reviewed and confirmed independently by 2 experienced pathologists. The automatic cell classification performance of the system was evaluated using 3 categories: accuracy, sensitivity, and specificity. Correlation coefficients and linear regression equations between automatic cell classification by the AI-based system and concurrent manual differential count were calculated. Results: In 230 cases, the classification accuracy was above 85.7% for hematopoietic lineage cells. Averages of sensitivity and specificity of the system were found to be 69.4 and 97.2%, respectively. The differential cell percentage of the automated count based on 200–500 cell counts was correlated with differential cell percentage provided by the pathologists for granulocytes, erythrocytes, and lymphocytes (r ≥ 0.762, p #x3c; 0.001). Discussion/Conclusion: This pilot study confirmed that the Morphogo system is a reliable tool for automatic bone marrow cell differential count analysis and has potential for clinical applications. Current ongoing large-scale multicenter validation studies will provide more information to further confirm the clinical utility of the system.
Acta Cytologica

中文翻译：

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Morphogo：一种通过人工智能分析的数字图像自动骨髓细胞分类系统。

简介：骨髓穿刺涂片上的有核细胞差异计数是血液系统恶性肿瘤临床诊断所必需的。手动骨髓差异计数耗时且缺乏一致性。在这项研究中，开发了一种新型的基于人工智能（AI）的系统来执行骨髓细胞的细胞自动分类并确定其潜在的临床应用。材料与方法：骨髓穿刺涂片取自陆军医科大学新桥医院。首先，一个自动化分析系统（ Morphogo）扫描并生成骨髓涂片的全数字图像。然后，通过使用基于AI的平台的软件分析放大倍数为1,000的涂片所选区域中的有核骨髓细胞。细胞分类结果由2位经验丰富的病理学家进一步审查和确认。使用3个类别评估系统的自动细胞分类性能：准确性，敏感性和特异性。计算了基于AI的系统自动进行细胞分类与同时进行的人工差分计数之间的相关系数和线性回归方程。结果：在230例中，造血谱系细胞的分类准确度高于85.7％。发现该系统的灵敏度和特异性的平均值分别为69.4％和97.2％。基于200-500细胞计数自动计数的差异细胞百分率与由病理学家对粒细胞，红细胞提供差异细胞百分比相关，和淋巴细胞（ř ≥0.762，p＃X3C; 0.001）。讨论/结论：这项初步研究证实了Morphogo该系统是用于自动骨髓细胞差异计数分析的可靠工具，具有临床应用潜力。当前正在进行的大规模多中心验证研究将提供更多信息，以进一步确认该系统的临床实用性。
细胞学学报