AUTHOR=Hunt Alexander , Schulze Holger , Samuel Kay , Fisher Robert B. , Bachmann Till T. 

TITLE=Stain-free artificial intelligence-assisted light microscopy for the identification of blood cells in microfluidic flow

JOURNAL=Frontiers in Bioinformatics

VOLUME=Volume 5 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1628724

DOI=10.3389/fbinf.2025.1628724

ISSN=2673-7647

ABSTRACT=The identification and classification of blood cells are essential for diagnosing and managing various haematological conditions. Haematology analysers typically perform full blood counts but often require follow-up tests such as blood smears. Traditional methods like stained blood smears are laborious and subjective. This study explores the application of artificial neural networks for rapid, automated, and objective classification of major blood cell types from unstained brightfield images. The YOLO v4 object detection architecture was trained on datasets comprising erythrocytes, echinocytes, lymphocytes, monocytes, neutrophils, and platelets imaged using a microfluidic flow system. Binary classification between erythrocytes and echinocytes achieved a network F1 score of 86%. Expanding to four classes (erythrocytes, echinocytes, leukocytes, platelets) yielded a network F1 score of 85%, with some misclassified leukocytes. Further separating leukocytes into lymphocytes, monocytes, and neutrophils, while also increasing the dataset and tweaking model parameters resulted in a network F1 score of 84.1%. Most importantly, the neural network’s performance was comparable to that of flow cytometry and haematology analysers when tested on donor samples. These findings demonstrate the potential of artificial intelligence for high-throughput morphological analysis of unstained blood cells, enabling rapid screening and diagnosis. Integrating this approach with microfluidics could streamline conventional techniques and provide a fast automated full blood count with morphological assessment without the requirement for sample handling. Further refinements by training on abnormal cells could facilitate early disease detection and treatment monitoring.