Abnormalities in white blood cell count are indicative of a blood disorder. Measuring these abnormalities is imperative. However, the use of flow cytometry and other conventional methods to assess white blood cell concentration is confined to hospital and laboratory settings due to the bulkiness and complexity of the equipment. Access to such equipment in remote areas presents challenges concerning early diagnosis for patients. Portable blood cell analyzers could alleviate these challenges and enable an earlier start on treatment. Researchers at Harbin Institute of Technology, Peking University, and the Beijing Institute of Collaborative Innovation developed a Smart Palm-sized Optofluidic Hematology Analyzer for automated, imaging-based leukocyte detection at the point of care. The device could improve diagnosis and treatment of blood diseases by allowing testing at home and in remote and low-resource regions of the world. Leukocytes in a channel captured by a miniature fluorescence microscope, with a magnified view of one of the cells and its profile curve (a). Working process of the particle counting algorithm (b). Scale bar: 100 μm. Courtesy of OES. The researchers built the portable device by constructing a miniature fluorescence microscope and integrating it with a compact, lightweight microfluidic platform. To enable the device to automatically detect leukocyte concentration and provide information about the cells, the researchers developed leukocyte counting algorithms and integrated the algorithms with image processing. The optofluidic hematology analyzer measures 35 × 30 × 80 mm and weighs 39 g, which is The miniaturized fluorescence microscope obtains the concentration of white blood cells in a sample by recording the stained white blood cells pumped into the field of view per unit time. It then uses a particle counting algorithm to quantify the number of cells. To test the efficacy of the Smart Palm-size Optofluidic Hematology Analyzer, the researchers used it to measure leukocyte concentration in blood samples and compared the results with the counting values from a benchtop hemocytometer. The team achieved a correlation coefficient of 0.979 using the Passing-Bablok regression analysis method. Through Bland-Altman analysis, they obtained the relationship between their differences and mean measurement values and established 95% limits of agreement, ranging from -0.93 × 103 to 0.94 × 103 cells/μL. The Smart Palm-size Optofluidic Hematology Analyzer calculated leukocyte concentration with an error rate of The researchers anticipate making several improvements to the Smart Palm-size Optofluidic Hematology Analyzer. To prevent out-of-focus visualization caused by oscillations during movement, they plan to upgrade the casing to integrate the miniaturized fluorescence microscope system more securely with the microfluidic chip. Additionally, to further optimize the overall size and weight of the system, they intend to reduce the dimensions of the microfluidic chips. The team also plans to develop particle identification and classification algorithms and integrate them with deep learning techniques. This will enable real-time classification of different types of white blood cells, providing more comprehensive information for disease diagnosis. In addition to identifying and monitoring blood disorders outside of hospital environments, the Smart Palm-size Optofluidic Hematology Analyzer could be used on space missions to provide valuable data for the fields of radiation biology and microgravity biology. In a spacecraft, where the environment is energy-intensive and resource-limited, the use of conventional equipment requiring chemical fuels and substantial space would increase costs significantly. The Smart Palm-size Optofluidic Hematology Analyzer would offer a potential solution by addressing the issues of volume and weight. The research was published in Opto-Electronic Science (www.doi.org/10.29026/oes.2023.230018).
