Leukocyte differential test is a widely performed clinical procedure for
screening infectious diseases. Existing hematology analyzers require
labor-intensive work and a panel of expensive reagents. Here we report
an artificial-intelligence enabled reagent-free imaging hematology
analyzer (AIRFIHA) modality 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, we not only achieved high accuracy in
differentiating B and T lymphocytes, but also classified CD4 and CD8
cells, therefore outperforming the classification accuracy of most
current hematology analyzers. We validated the performance of AIRFIHA in
a randomly selected test set and cross-validated it across all blood
donors. Owing to its easy operation, low cost, and accurate discerning
capability of complex leukocyte subpopulations, we envision 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.
Corresponding author(s) Email: rjzhou@cuhk.edu.hk,
rishikesh.pandey@uconn.edu
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