#AI reads Urine# Rapid culture-free diagnosis of clinical pathogens via integrated microfluidic-Raman micro-spectroscopy

Addressing the global health challenge of antimicrobial resistance (AMR), this study develops a culture-free diagnostic platform integrating a microfluidic enrichment system, Raman microspectroscopy, and a deep learning model. It enables pathogen detection from sample to report in just 20 minutes. The microfluidic system, leveraging dialysis-dielectrophoresis (DEP) technology, efficiently isolates pathogens from clinical samples with a detection limit as low as 2 colony-forming units (CFU)/ml. Combined with a Raman fingerprint database of 342 clinical isolates and a 1D ResNet model, the platform achieves 95.1% species identification accuracy in laboratory settings. Validated with clinical samples from 305 patients, it demonstrates 95.4% agreement with traditional culture methods and 98.5% sensitivity in infection diagnosis. Additionally, it effectively detects mixed infections and certain drug-resistant phenotypes, providing a rapid, sensitive, and broad-spectrum next-generation diagnostic solution for combating AMR. However, further expansion of sample types and sizes is required to promote its clinical application.

Nat Commun. 2025 Dec 16. doi: 10.1038/s41467-025-66996-y

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/