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

Year: 2025

Type: article

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.

Source: DOAJ (DOAJ: Directory of Open Access Journals)

DOI: 10.24423/cames.2025.1945

Authors: Redondo, Juan Alfonso, Paszek, Paweł

Institutions: Institute of Fundamental Technological Research, University of Manchester

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7108326919

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