微流控
分类
生物系统
人工神经网络
电阻抗
计算机科学
细胞仪
表征(材料科学)
粒子(生态学)
实验室晶片
跟踪(教育)
人工智能
纳米技术
材料科学
电子工程
化学
算法
工程类
电气工程
细胞
地质学
海洋学
生物
生物化学
教育学
心理学
作者
Carlos Honrado,John S. McGrath,Riccardo Reale,Paolo Bisegna,Nathan S. Swami,Federica Caselli
标识
DOI:10.1007/s00216-020-02497-9
摘要
Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting. Graphical Abstract.
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