CombiANT reader: Deep learning-based automatic image processing tool to robustly quantify antibiotic interactions

Antibiotic resistance is a severe danger to human health, and combination therapy with several antibiotics has emerged as a viable treatment option for multi-resistant strains. CombiANT is a recently developed agar plate-based assay where three reservoirs on the bottom of the plate create a diffusion landscape of three antibiotics that allows testing of the efficiency of antibiotic combinations. This test, however, requires manually assigning nine reference points to each plate, which can be prone to errors, especially when plates need to be graded in large batches and by different users. In this study, an automated deep learning-based image processing method is presented that can accurately segment bacterial growth and measure distances between key points on the CombiANT assay at sub-millimeter precision. The software was tested on 100 plates using photos captured by three different users with their mobile phone cameras, comparing the automated analysis with the human scoring. The result indicates significant agreement between the users and the software ([Formula: see text] mm mean absolute error) and remains consistent when applied to different photos of the same assay despite varying photo qualities and lighting conditions. The speed and robustness of the automated analysis could streamline clinical workflows and make it easier to tailor treatment to specific infections. It could also aid large-scale antibiotic research by quickly processing hundreds of experiments in batch, obtaining better data, and ultimately supporting the development of better treatment strategies. The software can easily be integrated into a potential smartphone application, making it accessible in resource-limited environments. Integrating deep learning-based smartphone image analysis with simple agar-based tests like CombiANT could unlock powerful tools for combating antibiotic resistance.