Rapidly Antibiotic Susceptibility Prediction via Deep Learning From Bacterial Fluorescence Microscopy Images

The incidence of multi-drug resistant bacterial species is rapidly increasing. To avoid antibiotic misuse and further exacerbation of this health crisis, clinicians should obtain a swift and accurate diagnosis of bacterial resistance. Existing laboratory-based antibiotic susceptibility testing (AST) methods are slow and laborious. Our study puts forward an efficient end-to-end image classification network using deep learning for predicting antibiotic resistance from fluorescence microscopy images of bacteria. Our resulting PAS-Net model integrates two parallel branches: the convolution branch (C-branch), employing ConvNeXt, and the Transformer branch (T-branch), utilizing Vision Transformer (ViT). To bridge these branches effectively, we introduce the feature interaction unit (FIU), which facilitates the integration of local features generated by the C-branch and global representations by the T-branch interactively. Additionally, we present a novel attention mechanism which reduces computational costs while maintaining the global representation capability. Experimental results, conducted on a fluorescence image dataset and public open datasets, suggest the promising potential of our method for rapidly diagnosing antibiotic susceptibility in bacteria and achieving high prediction performance. The code is publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Td270/PAS-Net</uri> Note to Practitioners—The rise of multi-drug resistant bacteria poses a significant challenge to public health, requiring faster and more accurate methods to predict bacterial antibiotic susceptibility. Current laboratory methods are slow and often impractical for time-sensitive clinical decisions. This work introduces PAS-Net, a deep learning framework designed to predict antibiotic resistance directly from fluorescence microscopy images of bacteria. Practitioners can apply this method in clinical and laboratory settings to significantly reduce diagnosis time, potentially enabling real-time decision-making. By combining convolutional and Transformer-based architectures, PAS-Net captures both detailed local patterns and global structural information, leading to improved predictive accuracy. This method is particularly suited for labs with access to fluorescence imaging equipment but limited by the slow throughput of traditional assays. However, the approach requires high-quality imaging and computational resources, which could limit its immediate application in low-resource settings. Future research should focus on adapting the model for broader imaging modalities and reducing its dependency on computational infrastructure. Beyond clinical diagnostics, the approach could be extended to other microscopy-based applications, such as microbial ecology or industrial bioprocess monitoring, where rapid and accurate phenotypic assessments are required.