Freeze–Thaw Imaging for Microorganism Classification Assisted with Artificial Intelligence

Fast and cost-effective microbial classification is crucial for clinical diagnosis, environmental monitoring, and food safety. However, traditional methods encounter challenges including intricate procedures, skilled personnel needs, and sophisticated instrumentations. Here, we propose a cost-effective microbe classification system, also termed freeze-thaw-induced floating pattern of AuNPs (FTFPA), coupled with artificial intelligence, which is capable of identifying microbes at a cost of $0.0023 per sample. Specifically, FTFPA utilizes AuNPs for coincubation with microbes, resulting in distinct patterns upon freeze-thawing due to their weak interaction. These patterns are digitized to train models that distinguish nine microbes in various tasks. The positive sample detection model achieved an F1 score of 0.976 (n = 194), while the multispecies classification task reached a macro F1 score of 0.859 (n = 1728). To address scalability and lightweight requirements across diverse classification scenarios, we categorized microbes based on species classification levels. The macro F1 score of the hierarchical model (n = 5184), order level model (n = 5184), Enterobacteriales level model (n = 2550), and Bacillales level model (n = 1974) was 0.854, 0.907, 0.958, and 0.843. In summary, our method is user-friendly, requiring only simple equipment, is easy to operate, and convenient, providing a platform for microbial identification.