Chronic infections with Helicobacter pylori (Hp) are the main risk factor for stomach cancer, which is one of the most prevalent causes of cancer-related deaths worldwide. Dependent on bacterial virulence factors, Hp profoundly remodels the actin cytoskeleton of host epithelial cells, yet translating these pheno types into scalable, quantitative readouts remains challenging. In this study, we employed deep learning to classify infection directly from immunofluorescence images and thereby provide a label-efficient proxy for virulence phenotyping. Epithelial monolayers were stained with DAPI (nuclei), phalloidin (F-actin), and a cellular protein marker and imaged under standardized conditions for three classes: non-infected, wild-type–infected, and mutant-infected cells. Single-channel and multi-channel models evaluated confirm that classifiers reliably distinguished all three states from images. These findings establish an image-based, AI enabled readout of virulence phenotypes suitable for scalable screening of pathogen-host interactions.