骨髓（BM）细胞的形态学检查在诊断和治疗各种血液学疾病中至关重要。然而，目前仍采用手动检查方式，工作量巨大。迫切需要一种人工智能辅助诊断系统来减轻检查者的工作量并提高结果的可重复性。本文提出了一种基于骨髓涂片形态学检查的人工智能辅助诊断支持系统，包括细胞检测、分类和白血病类型的预测。对于细胞检测，我们训练了新的YOLOX-s模型，以精确定位细胞并获取单个细胞图像。对于细胞分类，我们将其视为一项细粒度分类任务，并提出了一种称为MLFL-Net的新颖架构，利用多级特征。此外，我们根据世界卫生组织（WHO）标准对包括40名正常人（骨髓移植供体）和40名不同类型急性白血病患者的数据集进行白血病类型预测。我们构建了一个包含11,788张完全注释的显微图像和131,300张专家注释的单个细胞图像的大规模数据集。使用这个数据集，检测模型达到了0.9797的AUC和4.33％的框架定位误差。对于细胞分类，我们提出的MLFL-Net的总准确率达到了89.53％，在识别细胞类别方面优于所有其他相关模型。同时，我们以急性白血病为例，探索了血液病的白血病类型预测过程。对于92.5％的队列，它生成与专家给出的诊断预测相同的结果。这种人工智能辅助系统可用于协助临床决策并加速诊断。该方法将有助于促进BM细胞形态学的智能化和现代化，对于医学事业的发展具有重要意义。版权所有 ©2023 Elsevier B.V. 发布。

The morphological examination of bone marrow (BM) cells is essential in both diagnosing and treating various hematologic diseases. However, it is still done manually with a heavy workload. An artificial intelligence-assisted diagnosis support system of BM cells is highly required to reduce the workloads of examiners and improve the reproducibility of the results.In this paper, we proposed an artificial intelligence-assisted diagnosis support system of morphological examination based on bone marrow smears including cells detection, classification and prediction of leukemia types. For cell detection, we trained the novel YOLOX-s model to locate cells precisely and obtain single cell images. For cell classification, we regarded it as a fine- grained classification task and proposed a novel architecture called MLFL-Net utilizing multi-level features. Furthermore, we predicted the leukemia types on a dataset including 40 normal people (BM transplantation donors) and 40 patients of different kinds of acute leukemia according to the World Health Organization (WHO) standard.We constructed a large-scale data set of 11,788 fully-annotated micrographs from 728 smears and 131,300 expert-annotated single cell images. With the data set, the detection model achieved 0.9797 AUC and 4.33% box placement error. For cell classification, the total accuracy of our proposed MLFL-Net reached 89.53% which outperformed all the other related models in identifying cell categories. In the meantime, we took acute leukemia as an example to explore the leukemia types prediction procedure of hematological disease. It generated the same diagnostic prediction as the experts gave for 92.5 percent of the cohort.This Artificial Intelligence-assisted system can be implemented to aid in clinical decision making and accelerate diagnosis. The method will contribute to promote the intelligence and modernization of BM cytomorphology, which has vital significance of the development of the medical career.Copyright © 2023. Published by Elsevier B.V.