Using pathology images and artificial intelligence to identify bacterial infections and their types

Bacterial infections pose a significant biosafety concern, making early and accurate diagnosis essential for effective treatment and prognosis. Traditional diagnostic methods, while reliable, are often slow and fail to meet urgent clinical demands. In contrast, emerging technologies offer greater efficiency but are often costly and inaccessible. In this study, we utilized easily accessible pathology images to diagnose bacterial infections. Our initial findings indicate that, in the absence of postmortem phenomena, microscopic examination of pathological images can confirm the presence of a bacterial infection. However, distinguishing between different types of bacterial infections remains challenging due to similarities in pathological changes. To address this limitation, we applied a computational pathology approach by integrating pathology images with artificial intelligence (AI) algorithms. Our model classified bacterial infections at both the patch-level and whole slide image (WSI)-level. The results demonstrated strong performance, with an overall AUC consistently above 0.950 across training, testing, and external validation datasets, indicating high accuracy, robustness, and generalizability. This study highlights AI's potential in identifying bacterial infection types and provides valuable technical support for clinical diagnostics, paving the way for faster and more precise infection management.