Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver variability. The morphological differential diagnosis of abnormal lymphocytes from normal lymphocytes is still challenging. The digital pathology methods integrated with advances in machine learning enable new diagnostic features/algorithms from digital bone marrow cell images in order to optimize classification, thus providing a robust and faster screening diagnostic tool. We have developed a machine learning system, Morphogo, based on algorithms to discriminate abnormal lymphocytes from normal lymphocytes using digital imaging analysis. We retrospectively reviewed 347 cases of bone marrow digital images. Among them, 53 cases had a clinical history and the diagnosis of marrow involvement with lymphoma was confirmed either by morphology or flow cytometry. We split the 53 cases into two groups for training and testing with 43 and 10 cases, respectively. The selected 15,353 cell images were reviewed by pathologists, based on morphological visual appearance, from 43 patients whose diagnosis was confirmed by complementary tests. To expand the range and the precision of recognizing the lymphoid cells in the marrow by automated digital microscopy systems, we developed an algorithm that incorporated color and texture in addition to geometrical cytological features of the variable lymphocyte images which were applied as the training data set. The selected images from the 10 patients were analyzed by the trained artificial intelligence-based recognition system and compared with the final diagnosis rendered by pathologists. The positive predictive value for the identification of the categories of reactive/normal lymphocytes and abnormal lymphoid cells was 99.04%. It seems likely that further training and improvement of the algorithms will facilitate further subclassification of specific lineage subset pathology, e.g., diffuse large B-cell lymphoma from chronic lymphocytic leukemia/small lymphocytic lymphoma, follicular lymphoma, mantle cell lymphoma or even hairy cell leukemia in cases of abnormal malignant lymphocyte classes in the future. This research demonstrated the feasibility of digital pathology and emerging machine learning approaches to automatically diagnose lymphoma cells in the bone marrow based on cytological-histological analyses.
Acta Cytologica

中文翻译：

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使用数字显微镜 (Morphogo) 识别骨髓中异常淋巴细胞的机器学习工具

骨髓的形态学分析是诊断血液病的重要步骤。骨髓涂片的常规分析是在手动显微镜下进行的，这是劳动密集型的，并且受观察者间变异性的影响。异常淋巴细胞与正常淋巴细胞的形态学鉴别诊断仍然具有挑战性。数字病理学方法与机器学习的进步相结合，可以从数字骨髓细胞图像中获得新的诊断特征/算法，以优化分类，从而提供强大且更快的筛查诊断工具。我们开发了一个机器学习系统 Morphogo，它基于使用数字成像分析区分异常淋巴细胞和正常淋巴细胞的算法。我们回顾性地审查了 347 例骨髓数字图像。其中53例有临床病史，经形态学或流式细胞术确诊为骨髓受累淋巴瘤。我们将 53 个案例分成两组，分别用 43 个和 10 个案例进行训练和测试。病理学家根据形态学视觉外观审查了所选的 15,353 个细胞图像，这些图像来自 43 名通过补充测试确诊的患者。为了扩大自动数字显微镜系统识别骨髓中淋巴细胞的范围和精度，我们开发了一种算法，除了用作训练数据集的可变淋巴细胞图像的几何细胞学特征外，还结合了颜色和纹理。来自 10 名患者的选定图像由经过训练的基于人工智能的识别系统进行分析，并与病理学家给出的最终诊断结果进行比较。识别反应性/正常淋巴细胞和异常淋巴细胞类别的阳性预测值为99.04%。算法的进一步训练和改进似乎有可能促进特定谱系亚组病理学的进一步亚分类，例如，来自慢性淋巴细胞白血病/小淋巴细胞淋巴瘤的弥漫性大 B 细胞淋巴瘤、滤泡性淋巴瘤、套细胞淋巴瘤甚至毛细胞白血病未来恶性淋巴细胞类别异常的病例。
细胞学学报