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 leukocyte morphology change in presence of bacteria
  
JOURNAL=Frontiers in Bioinformatics
  
VOLUME=Volume 5 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1725145
  
DOI=10.3389/fbinf.2025.1725145
  
ISSN=2673-7647
  
ABSTRACT=BackgroundRapid detection of bacterial infections through leukocyte activation analysis could significantly reduce diagnostic timeframes from days to hours. Traditional methods like flow cytometry and biomarker assays face limitations including technical complexity, equipment requirements, and delayed results.MethodsWe developed a dual artificial neural network system combining stain-free light microscopy with microfluidic technology to detect morphological changes in activated leukocytes. YOLOv4 networks were trained using five-fold cross-validation on images of chemically stimulated leukocyte subpopulations (lymphocytes, monocytes, and neutrophils) and validated against flow cytometry. The system was tested on whole blood samples spiked with E. coli at clinically relevant concentrations (10–250 CFU/mL).ResultsThe optimized four-class network achieved high performance metrics for lymphocytes (F1 score: 80.1% ± 2.5%) and neutrophils (F1 score: 91.7% ± 1.7%), while a specialized binary classifier for monocytes achieved 97.0% ± 2.8% F1 score. In bacteria-spiked whole blood experiments, the system successfully detected activated leukocytes within 30 min at concentrations approaching clinical blood culture detection limits (11.11 ± 4.79 CFU/mL). Neutrophils showed rapid activation peaking at 1–3 h, while lymphocyte activation increased gradually over 6–12 h, consistent with innate versus adaptive immune response kinetics.ConclusionThis AI-assisted microscopy platform enables rapid, label-free detection of leukocyte activation in response to bacterial infection with minimal sample handling and no requirement for specialized staining or trained technicians. The technology demonstrates potential for accelerating infection diagnosis and could be extended to other pathologies with morphological manifestations.