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Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone
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一个基于机器学习的算法用于骨髓细胞差异计数.

Ta-Chuan Yu1, Cheng-Kun Yang2, Wei-Han Hsu2

  • 1Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, No. 579, Sec. 2, Yunlin Rd., Douliu City, Yunlin County 640203, Taiwan.

International journal of medical informatics
|November 16, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种人工智能算法,用于自动化骨髓微分计数,在识别和分类各种细胞类型时实现高精度. 开发的AI显示了在诊断血液病的临床应用的潜力.

关键词:
人工智能的人工智能是人工智能.血细胞计数是指血液中的细胞数量.骨髓检查 骨髓检查 骨髓检查

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科学领域:

  • 血液学 血液学 血液学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 骨髓分数计数对于诊断血液病至关重要.
  • 目前的手工方法耗时且主观.
  • 需要一个自动化的,临床上适用的差分计数解决方案.

研究的目的:

  • 开发和验证基于人工智能 (AI) 的算法,用于在骨髓涂抹中自动识别和分类核细胞.
  • 评估AI算法用于骨髓微分计数的临床适用性.

主要方法:

  • 一个基于Mask R-CNN的AI模型被训练在大量专家注释的骨髓细胞图像的数据集上.
  • 图像被染色的染色或赖特-吉姆萨染色,专家的共识确保一致的分类标准.
  • 人工智能算法的性能在多国临床数据集上进行了评估,用于细胞识别和微分计数比率.

主要成果:

  • 人工智能模型在测试数据集上实现了0.94的准确性,在多国实体数据集上达到0.881的准确性.
  • 在分类特定细胞类型时观察到高精度,包括爆发 (0.927) 和中性粒细胞 (0.955).
  • 在大多数细胞类型中,AI和手动差异计数百分比之间发现了强烈的相关性 (ρ > 0.8).

结论:

  • 一种用于骨髓微分计数的AI算法已成功开发并经过临床验证.
  • 该算法可以同时定位和分类骨髓细胞.
  • 这种人工智能工具在临床实践中具有自动化骨髓微分计数的巨大潜力.