Raji Hassan, Tayyab Muhammad, Sui Jianye, Mahmoodi Seyed Reza, Javanmard Mehdi
Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
Biomed Microdevices. 2022 Aug 12;24(3):26. doi: 10.1007/s10544-022-00627-x.
Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.
根据定义，生物细胞是包含构成所有生物的基本生命分子的基本单位。因此，了解它们如何发挥功能以及区分不同细胞，对于疾病诊断和治疗至关重要。随着技术进步使得各种组件得以小型化，让我们日益接近即时检测（POC）解决方案，专注于细胞检测和分层的传感器越来越受欢迎。此外，机器学习增强了这些各种生物传感方式的分析能力，特别是使用数据驱动方法而非物理驱动方法将细胞分类到不同类别的具有挑战性的任务。在本综述中，我们阐述了机器学习如何已被明确应用于检测和分类细胞的传感器。我们还比较了不同的传感方式和算法如何影响分类器准确性以及所需的数据集大小。
