An integrated microflow cytometry platform with artificial intelligence capabilities for point-of-care cellular...

Area of Science:

• Biomedical Engineering
• Cell Biology
• Artificial Intelligence

Background:

• Traditional cell analysis methods can be time-consuming and require manual intervention.
• Automated platforms are needed for high-throughput and precise cell phenotyping and enumeration.

Purpose of the Study:

• To introduce and evaluate the EZ DEVICE, an integrated fluorescence microflow cytometer.
• To demonstrate its capabilities in automated cell analysis using artificial intelligence (AI).

Main Methods:

• The EZ DEVICE integrates a laser, optics, CMOS sensor, and microfluidic chip for automated sample processing and detection.
• AI algorithms were employed for image segmentation and object identification.
• Performance was validated using fluorescent beads and immune cell lines with specific antibodies.

Main Results:

• The device demonstrated accurate counting performance comparable to manual microscopy.
• Achieved 99.06% antibody-staining efficiency.
• Successfully phenotyped immune cell lines, detecting MHC I and CD3 expression patterns.

Conclusions:

• The EZ DEVICE offers a compact, automated solution for cell analysis with high efficiency.
• It shows significant potential for clinical diagnostics, point-of-care testing, and research applications.
• Future work will focus on enhancing throughput and expanding its utility in personalized medicine.