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AI offers a faster way to predict antibiotic resistance

The intelligent computer model learned to predict antibiotic resistance on its own using data from over 300,000 bacterial samples. (Symbol image: Adobe Stock)

A study under co-​leadership of the ETH Zurich has shown that computer algorithms can determine antimicrobial resistance of bacteria faster than previous methods. This could help treat serious infections more efficiently in the future.

Antibiotic-​resistant bacteria are on the rise all over the world – and Switzerland is no exception. Each year, infections caused by multi-​drug resistant bacteria lead to at least 300 fatalities in Switzerland alone. Rapid diagnostic testing and the targeted use of antibiotics play a crucial role in curbing the spread of these antibiotic-​resistant “superbugs”. However, it often takes two or more days to determine which antibiotics are still effective against a particular pathogen because the bacteria from the patient’s sample first have to be cultivated in the diagnostic lab.

Now, researchers at ETH Zurich, the University Hospital Basel and the University Basel have developed a method that uses mass spectrometry data to identify signs of antibiotic resistance in bacteria up to 24 hours earlier.

By identifying significant antibiotic resistances at an early stage, doctors can tailor an antibiotic therapy to the relevant bacterium more quickly. This can be particularly beneficial for seriously ill patients.

ETH News