Machine learning implementation strategy in imaging and impedance flow cytometry.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Cell Biology

Background:

• Imaging and impedance flow cytometry is a label-free, high-throughput technique.
• It offers rich data potential, surpassing standard flow cytometry.
• Machine learning (ML) is increasingly used for analyzing complex data from these methods.

Purpose of the Study:

• To provide a comprehensive overview of ML implementation in imaging and impedance flow cytometry.
• To detail strategies for data acquisition, feature extraction, and ML-based cell phenotyping.
• To discuss current challenges and future directions in intelligent flow cytometry.

Main Methods:

• Overview of data acquisition setups for imaging and impedance flow cytometry.
• Description of feature extraction techniques from cell images and impedance signals.
• Explanation of ML algorithms for cell phenotyping using extracted features.

Main Results:

• ML enables rapid and accurate analysis of complex cell populations.
• Successful application of ML for advanced cell phenotyping scenarios.
• Demonstrated potential to overcome limitations of standard flow cytometry.

Conclusions:

• ML integration significantly enhances imaging and impedance flow cytometry capabilities.
• The discussed strategies facilitate advanced cell analysis and phenotyping.
• Future work should focus on addressing existing challenges for broader adoption.