Abstract
Leukocyte differential test is a widely carried out clinical procedure for screening infectious diseases. Existing hematology analyzers require labor‐intensive work and a panel of expensive reagents. Herein, an artificial‐intelligence‐enabled reagent‐free imaging hematology analyzer (AIRFIHA) modality is reported that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two‐step residual neural network using label‐free images of isolated leukocytes acquired from a custom‐built quantitative phase microscope. By leveraging the rich information contained in quantitative phase images, not only high accuracy is achieved in differentiating B and T lymphocytes, but also CD4 and CD8 T cells are classified, therefore outperforming the classification accuracy of most current hematology analyzers. The performance of AIRFIHA in a randomly selected test set is validated and is cross‐validated across all blood donors. Due to its easy operation, low cost, and accurate discerning capability of complex leukocyte subpopulations, AIRFIHA is clinically translatable and can also be deployed in resource‐limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases.
Highlights
Leukocytes play an important role in maintaining the normal function of human immune systems
The exact configuration of the Quantitative phase microscopy (QPM) system is based on a diffraction phase microscope (DPM)[2,39,40], which can provide highly stable and accurate phase imaging of cells
The cross-donor validation results have shown that our method has a high potential for clinical applications. In this proof-of-concept study, the capability of artificial-intelligence enabled reagentfree imaging hematology analyzer (AIRFIHA) for label-free classification of leukocyte subpopulations has been demonstrated on human blood donors
Summary
Leukocytes play an important role in maintaining the normal function of human immune systems. B and T lymphocytes can produce antibodies to defend the body against foreign substances, such as bacteria and viruses. Abnormal leukocyte differential counts are indications of malfunctions of the immune system or infectious diseases[1]. To differentiate basic leukocyte types, volume and granularity parameters are often measured through electrical impedance and light scattering-based cytometry techniques[5]. For more complex leukocyte types with similar morphologies (e.g., B and T lymphocytes), fluorescent molecules bound with antibodies that target the proteins expressed on the surface are typically used to activate fluorescence emission which can be captured by detectors for population counting. Antibody labeling based flow cytometry methods are widely used in the clinical laboratories, there remains a few drawbacks. An extensive list of expensive reagents is required for differentiating more cell types. Dyes are susceptible to photobleaching which can affect long-term observation results
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