• Opto-Electronic Science
  • Vol. 2, Issue 12, 230018 (2023)
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
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    DOI: 10.29026/oes.2023.230018 Cite this Article
    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 Copy Citation Text show less
    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.
    Fig. 1. 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.
    Fig. 2. (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.
    Fig. 3. (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.
    Fig. 4. (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.
    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
    Table 1. Standard values, average values, and errors of white blood cell concentration for partial samples.
    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
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