ORIGINAL RESEARCH article
Front. Bioinform.
Sec. Computational BioImaging
This article is part of the Research TopicAI in Computational BioimagingView all articles
Stain-free artificial intelligence-assisted light microscopy for the identification of leukocyte morphology change in presence of bacteria
Provisionally accepted
- 1Division of Infection Medicine, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
- 2Scottish National Blood Transfusion Service, Edinburgh, United Kingdom
- 3The University of Edinburgh, Edinburgh, United Kingdom
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Background: Rapid 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. Methods: We 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). Results: The 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 minutes at concentrations approaching clinical blood culture detection limits (11.11 ± 4.79 CFU/mL). Neutrophils showed rapid activation peaking at 1-3 hours, while lymphocyte activation increased gradually over 6-12 hours, consistent with innate versus adaptive immune response kinetics. Conclusion: This 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.
Keywords: artificial neural network, morphological analysis, YOLO v4, Blood analysis, leukocyte
Received: 14 Oct 2025; Accepted: 24 Nov 2025.
Copyright: © 2025 Hunt, Schulze, Samuel, Fisher and Bachmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Alexander Hunt
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