Inferring ciprofloxacin susceptibility in salmonella with AI and imaging. Cambridge researchers have shown that artificial intelligence (AI) can quickly identify drug-resistant infections from microscope images, reducing diagnosis time. The AI can spot subtle features in images humans can’t see, quickly distinguishing resistant bacteria.
Antimicrobial resistance is a growing global health problem, making many infections hard to treat. One major challenge is identifying whether bacteria resist first-line drugs, as traditional testing can take days. This delay often leads to incorrect treatments, worsening patient outcomes and contributing to increased drug resistance.
Researchers from Professor Stephen Baker’s Lab at the University of Cambridge created a machine-learning tool to identify ciprofloxacin-resistant Salmonella Typhimurium from microscopy images without testing the bacteria against the drug. S. Typhimurium can cause serious illness with symptoms like fever , headache, and abdominal pain .
In severe cases, it can be life-threatening. While antibiotics can treat infections, the bacteria are becoming more resistant, making treatment more challenging. The team used high-resolution microscopy to study S.
Typhimurium exposed to ciprofloxacin and identified the top five imaging features that indicate resistance. Using data from 16 samples, they trained a machine-learning algorithm to recognize these features. The algorithm correctly predicted whether bacteria were resi.
