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Artificial  Intelligence Enabled Reagent-free Imaging Hematology Analyzer            
  • +4
  • Xin Shu,
  • Sameera Sansare,
  • Di Jin,
  • Xiangxiang Zhang,
  • Kai-Yu Tong,
  • Rishikesh Pandey,
  • Renjie Zhou
Xin Shu
Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

Corresponding Author:[email protected]

Author Profile
Sameera Sansare
Connecticut Children’s Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut, 06032, USA, Department of Pharmaceutical Sciences, University of Connecticut Storrs, Connecticut, 06269, USA
Di Jin
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Cambridge, Massachusetts, 02139, USA
Xiangxiang Zhang
School of Information Science and Engineering, Hunan University, Changsha, 410076, China
Kai-Yu Tong
Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Rishikesh Pandey
Connecticut Children’s Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut, 06032, USA, Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, 06269, USA
Renjie Zhou
Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China, Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

Abstract

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 donorsOwing 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:    [email protected][email protected]
31 Oct 2021Submitted to AISY Interactive Papers
31 Oct 2021Published in AISY Interactive Papers
Aug 2021Published in Advanced Intelligent Systems volume 3 issue 8 on pages 2000277. 10.1002/aisy.202000277