Artificial Intelligence Enabled Reagent-free Imaging Hematology Analyzer

TL;DR

This paper introduces AIRFIHA, an AI-powered, reagent-free imaging system that accurately classifies leukocyte subpopulations using label-free microscopy, offering a low-cost, easy-to-operate alternative for clinical blood analysis.

Contribution

The study presents a novel AI-enabled hematology analyzer that classifies leukocytes without reagents, outperforming existing methods in accuracy and suitability for resource-limited settings.

Findings

Outperforms current methods in classifying B and T lymphocytes.

Accurately differentiates CD4 and CD8 cells.

Operates with minimal sample preparation and low cost.

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 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.