AI-Assisted Microscopy for Infection Biology: Advances in High-Content Imaging of Host-Pathogen Interactions

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Authors

  • Juan Alfonso REDONDO Institute of Fundamental Technological Research Polish Academy of Sciences, Warsaw, Poland
  • Pawel PASZEK Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland / The Lydia Becker Institute of Immunology and Inflammation, University of Manchester, Manchester, UK / Division of Immunology, Immunity to Infection and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom

Abstract

Advances in high-content microscopy and artificial intelligence (AI) are transforming the quantitative study of infection biology. Automated imaging platforms now enable rapid, large-scale acquisition of host-pathogen interactions across thousands of cells and multiple experimental conditions. When combined with AI-based segmentation, these workflows extract infection-relevant features such as pathogen load, intracellular localization, and host response markers at single-cell resolution. Deep-learning models have proven especially powerful, outperforming classical threshold-based methods under different imaging conditions, reducing reliance on manual annotation, and detecting rare infection outcomes. Beyond robust image analysis, these approaches generate scalable and reproducible datasets that can be integrated with computational modelling and systems biology, providing predictive insight into infection dynamics. This review highlights recent progress in AI-assisted microscopy for bacterial infection and outlines future directions toward multimodal integration, clinical translation, and open-source tool development.

Keywords:

artificial intelligence, machine learning, deep learning, host-pathogen interactions, single-cell biology, cell-to-cell variability, cellular heterogeneity, infection biology

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