A review on intelligent impedance cytometry systems: Development, applications and advances

Area of Science:

• Biomedical Engineering
• Cell Biology
• Analytical Chemistry

Background:

• Impedance cytometry is a label-free, high-throughput method for single-cell analysis.
• Standard experiments involve cell measurement, signal processing, data calibration, and subtype identification.

Purpose of the Study:

• To provide a comprehensive review of impedance cytometry techniques.
• To discuss advancements in intelligent impedance cytometry and machine learning applications.
• To identify current challenges and future directions in the field.

Main Methods:

• Comparison of commercial and self-developed impedance cytometry systems.
• Analysis of impedance metrics and their relation to cell biophysical properties.
• Review of machine learning approaches for data calibration and particle identification.

Main Results:

• Established impedance cytometry as a versatile tool for cell analysis.
• Highlighted the integration of machine learning for enhanced data processing and identification.
• Detailed the relationship between impedance signals and cellular characteristics.

Conclusions:

• Impedance cytometry is a powerful technique for cell counting and analysis.
• Machine learning significantly improves data calibration and particle identification in impedance cytometry.
• Future research should focus on refining detection systems and advancing intelligent algorithms.