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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

概括

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.

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.

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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相关概念视频

Flow Cytometry

Flow Cytometry

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.
In...