Open AccessArtificial‐Intelligence‐Enabled Reagent‐Free Imaging Hematology Analyzer
Author(s) -
Shu Xin,
Sansare Sameera,
Jin Di,
Zeng Xiangxiang,
Tong Kai-Yu,
Pandey Rishikesh,
Zhou Renjie
Publication year - 2021
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202000277
Subject(s) - hematology analyzer , hematology , computer science , artificial intelligence , blood smear , pattern recognition (psychology) , biomedical engineering , medicine , pathology , medical physics , malaria
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.