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相关概念视频

Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Updated: Jul 22, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

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人工智能用于临床流细胞计.

Robert P Seifert1, David A Gorlin2, Andrew A Borkowski3

  • 1Department of Pathology, Immunology and Laboratory Medicine, University of Florida, College of Medicine, 4800 Southwest 35th Drive, Gainesville, FL 32608, USA.

Clinics in laboratory medicine
|July 22, 2023
PubMed
概括
此摘要是机器生成的。

机器学习在流细胞测量中显示出希望,但面临挑战. 关键问题包括"黑子"算法,这些算法阻碍了可解释性,并且在没有协作开发的情况下,潜在的概括性很差.

关键词:
人工智能的人工智能是人工智能.临床流动细胞计量 临床流动细胞计量流动细胞计量流动细胞计量血液病理学 血液病理学机器学习 机器学习

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科学领域:

  • * 计算生物学 * 计算生物学
  • * 数据科学数据科学
  • * 免疫学 免疫学

背景情况:

  • * 机器学习 (ML) 提供先进的分析能力.
  • * 流细胞计产生复杂的,高维的数据集.
  • *将ML集成到流细胞计工作流中是一个新兴的研究领域.

研究的目的:

  • * 审查机器学习的基本原则.
  • * 探索ML在流细胞计中的近期应用.
  • * 确定 ML 在这个领域的挑战和局限性.

主要方法:

  • * 关于机器学习应用在流细胞计中的当前文献的综述.
  • * 讨论机器学习算法背后的原则.
  • *对流细胞计数据现有ML模型的优缺点进行分析.

主要成果:

  • *ML在流细胞计中的应用对数据分析具有前景.
  • * 机器学习模型的可解释性 ("黑子"问题) 是其采用的主要障碍.
  • * 在没有多机构发展的情况下,ML模型的普遍性不佳是令人担忧的.

结论:

  • *克服可解释性挑战对于在流细胞计学中广泛采用ML至关重要.
  • * 需要合作,多机构的努力来提高ML模型的通用性.
  • *未来的方向包括部署增强型决策工具.