Artificial intelligence (AI) could be used to identify drug resistant infections, significantly reducing the time taken for a correct diagnosis, Cambridge researchers have shown. The team showed that an algorithm could be trained to identify drug-resistant bacteria correctly from microscopy images alone.

Antimicrobial resistance is an increasing global health issue that means many infections are becoming difficult to treat, with fewer treatment options available. It even raises the spectre of some infections becoming untreatable in the near future.

One of the challenges facing healthcare workers is the ability to distinguish rapidly between organisms that can be treated with first-line drugs and those that are resistant to treatment. Conventional testing can take several days, requiring bacteria to be cultured, tested against various antimicrobial treatments, and analysed by a laboratory technician or by machine. This delay often results in patients being treated with an inappropriate drug, which can lead to more serious outcomes and, potentially, further drive drug resistance.

In research published in Nature Communications, a team led by researchers in Professor Stephen Baker’s Lab at the University of Cambridge developed a machine-learning tool capable of identifying from microscopy images Salmonella Typhimurium bacteria that are resistant to the first-line antibiotic ciprofloxacin – even without testing the bacteria against the drug.

S. Typhimurium causes gastrointestinal illness and typhoid-like illness in severe cases, whose symptoms include fever, fatigue, headache, nausea, abdominal pain, and constipation or diarrhoea. In severe cases, it can be life threatening. While infections can be treated with antibiotics, the bacteria are becoming increasingly resistant to a number of antibiotics, making treatment more complicated.

The team used high-resolution microscopy to examine S. Typhimurium isolates exposed to increasing concentrations of ciprofloxacin and identified the five most important imaging features for distinguishing between resistant and susceptible isolates.

They then trained and tested machine-learning algorithm to recognise these features using imaging data from 16 samples.

The algorithm was able to correctly predict in each case whether or not bacteria were susceptible or resistant to ciprofloxacin without the need for the bacteria to be exposed to the drug. This was the case for isolates cultured for just six hours, compared to the usual 24 hours to culture a sample in the presence of antibiotic.