  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 separated
leukocytes acquired from a custom-built quantitative phase microscope. We
validated the performance of AIRFIHA in randomly selected test set and
cross-validated it across all blood donors. AIRFIHA outperforms current methods
in classification accuracy, especially in B and T lymphocytes, while preserving
the natural state of cells. It also shows a promising potential in
differentiating CD4 and CD8 cells. Owing to its easy operation, low cost, and
strong 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.
