急性髓性白血病（AML）是一种致命的血液恶性肿瘤。基于法美英（FAB）分类系统的骨髓涂片细胞形态学检测仍然是诊断血液恶性肿瘤的重要标准。然而，从骨髓涂片图像获取不同FAB亚型的AML的诊断和鉴别是繁琐和耗时的。此外，在农村地区，病理学家可能没有相关的专业知识，他们之间存在相当大的差异。我们在此基于2010年至2021年的回顾性双中心研究建立了一个包含651位患者8245张骨髓涂片图像的全面数据库，旨在用于培训和测试。此外，我们开发了一种基于骨髓涂片图像的深度学习流水线AMLnet，它不仅可以区分AML患者和健康人群，还可以准确识别各种AML亚型。AMLnet在测试数据集上在图像级别和患者级别分别实现了0.885和0.921的AUC，在区分9种AML亚型方面表现出色。此外，AMLnet在患者级别上的表现优于初级专家，并与高级专家相当。最后，我们提供了一个交互式演示网站，用于可视化AMLnet的显着映射和结果，以辅助病理学家的诊断。总之，AMLnet有潜力成为细胞形态学病理学家的快速预筛选和决策支持工具，特别是在病理学家面对过多医疗需求或在医疗资源匮乏的农村地区。 © 2023年。作者（们）。

Acute myeloid leukemia (AML) is a deadly hematological malignancy. Cellular morphology detection of bone marrow smears based on the French-American-British (FAB) classification system remains an essential criterion in the diagnosis of hematological malignancies. However, the diagnosis and discrimination of distinct FAB subtypes of AML obtained from bone marrow smear images are tedious and time-consuming. In addition, there is considerable variation within and among pathologists, particularly in rural areas, where pathologists may not have relevant expertise. Here, we established a comprehensive database encompassing 8245 bone marrow smear images from 651 patients based on a retrospective dual-center study between 2010 and 2021 for the purpose of training and testing. Furthermore, we developed AMLnet, a deep-learning pipeline based on bone marrow smear images, that can discriminate not only between AML patients and healthy individuals but also accurately identify various AML subtypes. AMLnet achieved an AUC of 0.885 at the image level and 0.921 at the patient level in distinguishing nine AML subtypes on the test dataset. Furthermore, AMLnet outperformed junior human experts and was comparable to senior experts on the test dataset at the patient level. Finally, we provided an interactive demo website to visualize the saliency maps and the results of AMLnet for aiding pathologists' diagnosis. Collectively, AMLnet has the potential to serve as a fast prescreening and decision support tool for cytomorphological pathologists, especially in areas where pathologists are overburdened by medical demands as well as in rural areas where medical resources are scarce.© 2023. The Author(s).