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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Updated: May 7, 2026

Wide-field Fluorescent Microscopy and Fluorescent Imaging Flow Cytometry on a Cell-phone
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Pump-Free Microfluidics for Cell Concentration Analysis on Smartphones in Clinical Settings (SmartFlow): Design,

Sixuan Wu1, Kefan Song2, Jason Cobb3

  • 1School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.

JMIR Biomedical Engineering
|December 23, 2024
PubMed
Summary

SmartFlow uses smartphone computer vision on 3D-printed microfluidic chips for low-cost cell counting and concentration analysis. This pump-free system leverages gravity-driven flow and achieves high accuracy in estimating cell concentrations from videos.

Keywords:
cellcells counting, body fluid analysis, blood test, urinalysis, computer vision, machine learningcellularchipconcentrationfluidmHealthmicrofluidicsmicroscopemobile healthsmartphoneubiquitous health

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Area of Science:

  • Biomedical Engineering
  • Microfluidics
  • Computer Vision
  • Point-of-Care Diagnostics

Background:

  • Accurate cell concentration in body fluid is crucial for clinical diagnosis.
  • Traditional manual cell counting is labor-intensive and automated methods like flow cytometry are expensive.
  • Existing microfluidic systems often require costly high-speed cameras and pumps.

Purpose of the Study:

  • To design and fabricate low-cost, pump-free microfluidic chips for cell counting and concentration analysis.
  • To investigate if gravity can drive flow in microfluidic chips, eliminating the need for external pumps.
  • To assess the impact of microfluidic chip design on video quality for smartphone-based analysis.
  • To determine the feasibility of using smartphone-captured videos for accurate cell count and concentration estimation.

Main Methods:

  • Fabrication of 3D-printed microfluidic chips with and without bottleneck designs.
  • Experiments to evaluate gravity-driven flow velocity and its impact on cell movement.
  • Analysis of video quality in relation to chip height differences and bottleneck presence.
  • Smartphone video recording of sheep blood samples at 13 different concentrations for analysis.
  • Cell counting and concentration estimation using computer vision algorithms with 5-fold cross-validation.

Main Results:

  • Gravity was confirmed to drive flow in microfluidic chips, with height significantly impacting cell velocity (P<.001).
  • Video quality exhibited exponential decay with increasing height differences, but bottleneck designs preserved quality (R²=0.91).
  • Smartphone-based computer vision achieved high accuracy for cell counting (R²=0.96) and concentration estimation (R²=0.99).

Conclusions:

  • The SmartFlow system, utilizing smartphone computer vision on pump-free microfluidic platforms, offers a low-cost solution for cellular analysis.
  • The system successfully demonstrates accurate cell counting and concentration estimation, highlighting the potential for accessible point-of-care diagnostics.