AI offers a fast way to predict antibiotic resistance

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. Due to this delay, many doctors initially treat serious infections with a class of drugs known as broad-​spectrum antibiotics, which are effective against a broad range of bacterial species.

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.

“Intelligent computer algorithms search the data for patterns that distinguish resistant bacteria from those that are responsive to antibiotics,” says Caroline Weis, a doctoral student in the Department of Biosystems Science and Engineering at ETH Zurich in Basel and the study’s lead author. The researchers published their method in the latest issue of the journal Nature Medicine.