Lin Yifei, Chen Qingquan, Chen Tebin
The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China.
Front Med (Lausanne). 2024 Jun 14;11:1402768. doi: 10.3389/fmed.2024.1402768. eCollection 2024.
As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.
随着机器学习的发展，神经网络、决策树和支持向量机等技术在医学领域的应用越来越广泛，特别是用于处理涉及大型数据集的任务，如细胞检测、识别、分类和可视化。在骨髓细胞形态分析领域，深度学习因其鲁棒性、自动特征学习能力和强大的图像表征能力而具有显著优势。深度神经网络是一种专门为图像处理应用量身定制的机器学习范式。人工智能是支持临床骨髓细胞形态诊断过程的有力工具。尽管人工智能在该领域增强临床诊断方面具有潜力，但骨髓细胞形态的手动分析仍然是识别、诊断和评估血液系统疾病疗效的金标准和不可或缺的工具。然而，传统的手动方法并非没有局限性和缺点，因此有必要探索用于检查和分析骨髓细胞形态的自动化解决方案。本综述从多个维度阐述了六个骨髓细胞形态学过程：自动化骨髓细胞形态检测、自动化骨髓细胞形态分割、自动化骨髓细胞形态识别、自动化骨髓细胞形态分类、自动化骨髓细胞形态计数和自动化骨髓细胞形态诊断。该综述突出了基于骨髓细胞形态的机器学习系统的吸引力和潜力，综合了机器学习在该领域应用的当前研究和最新进展。本综述的目的是为血液学家提供建议，以选择最合适的机器学习算法来自动化骨髓细胞形态检查，从而能够快速、准确地分析骨髓细胞病变趋势，实现疾病的早期识别和诊断。此外，该综述还努力描绘基于机器学习的应用在骨髓细胞形态分析方面未来潜在的研究方向。
