Opto-Electronic Science, Volume. 2, Issue 12, 230018(2023)

Smart palm-size optofluidic hematology analyzer for automated imaging-based leukocyte concentration detection

Deer Su1, Xiangyu Li2, Weida Gao3, Qiuhua Wei4, Haoyu Li1, Changliang Guo5,6、*, and Weisong Zhao1、**
Author Affiliations
  • 1Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
  • 2Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150081, China
  • 3Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
  • 4Institute of Optical Measurement and Intellectualization, Harbin Institute of Technology, Harbin 150080, China
  • 5Beijing Institute of Collaborative Innovation, Beijing 100094, China
  • 6State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing 100871, China
  • show less
    Figures & Tables(5)
    Principle and construction of the Palm-size Optofluidic Hematology Analyzer. (a) The photograph and model diagrams of the Palm-size Optofluidic Hematology Analyzer. (b) The model diagram of a miniature fluorescence microscope. (c) The optical path design of a miniature fluorescence microscope. (d) Results of the USAF target at various axial positions imaged by the miniature fluorescence microscope. Z stands for object distance. (e) The lateral resolution of miniature fluorescence microscope as a function of object distance. (f) The model diagram of the designed microfluidic chip with top and front views of the profiles.
    (a) Particle centroid tracking principle. (b) Flow chart of image pre-processing. (c) Flow chart of particle counting.
    (a) Leukocytes in a channel captured by a miniature fluorescence microscope, with a magnified view of one of the cells and its profile curve. (b) Working process of the particle counting algorithm.
    (a) Scatter diagram and regression equation of total white blood cells from both methods analyzed by Passing-Bablok regression analysis, sample number = 40. Regression equation: y = 0.9926 x + 0.0678, correlation coefficient R = 0.979; 95% confidence interval for slope 0.7955 to 1.0304 and for intercept –0.4380 to 0.9189. (b) The Bland-Altman analysis between the average and difference of the total white blood cells calculated by the two methods. The orange and yellow lines represent the upper and lower LOA, respectively, and the purple line represents the bias of the average count difference from 0. (c) The white blood cell counting results obtained from a patient's whole blood using our Palm-size Optofluidic Hematology Analyzer. The orange line represents the average of 10-count results, and the yellow line represents the standard value obtained by the hemocytometer.
    • Table 1. Standard values, average values, and errors of white blood cell concentration for partial samples.

      View table
      View in Article

      Table 1. Standard values, average values, and errors of white blood cell concentration for partial samples.

      Sample numberAverage values (103/μL)Standard values (103/μL)Error (%)
      13.023.308.48
      23.533.714.85
      36.036.385.49
      47.738.053.98
      58.889.355.03
    Tools

    Get Citation

    Copy Citation Text

    Deer Su, Xiangyu Li, Weida Gao, Qiuhua Wei, Haoyu Li, Changliang Guo, Weisong Zhao. Smart palm-size optofluidic hematology analyzer for automated imaging-based leukocyte concentration detection[J]. Opto-Electronic Science, 2023, 2(12): 230018

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: Jul. 30, 2023

    Accepted: Oct. 9, 2023

    Published Online: Mar. 19, 2024

    The Author Email: Guo Changliang (CLGuo), Zhao Weisong (WSZhao)

    DOI:10.29026/oes.2023.230018

    Topics