Using artificial intelligence to treat infections more accurately

New research from the Centres for Antimicrobial Optimisation Network (CAMO-Net) at the University of Liverpool has shown that using artificial intelligence (AI) can improve how we treat urinary tract infections (UTIs), and help to address antimicrobial resistance (AMR).

AMR occurs when bacteria, viruses, fungi, and parasites evolve and no longer respond to treatments that were once effective. This resistance leads to longer hospital stays, higher medical costs, and increased mortality rates, posing a significant threat to public health and potentially rendering common infections untreatable.

Traditional UTI diagnostic tests, known as antimicrobial susceptibility testing (AST), uses a one-size-fits-all approach to determine which antibiotics are most effective against a specific bacterial or fungal infection. This new research, published in Nature Communications, proposes a personalised method, using real-time data to help clinicians target infections more accurately and reduce the chance of bacteria becoming resistant to antibiotic treatment.

The research, led by Dr Alex Howard, a consultant in medical microbiology at the University of Liverpool and researcher on the Wellcome Trust funded CAMO-Net, used AI to test prediction models for 12 antibiotics using real patient data and compared personalised AST with standard methods. The data-driven personalised approach led to more accurate treatment options, especially with WHO Access antibiotics, known for being less likely to cause resistance.

Dr Alex Howard, said: “This research is important and timely for World AMR Awareness Week because it shows how combining routine health data with lab tests can help keep antibiotics working. By using AI to predict when people with urine infections have antibiotic-resistant bugs, we show how lab tests can better direct their antibiotic treatment. This approach could improve the care of people with infections worldwide and help prevent the spread of antibiotic resistance.”

The results of this study represent a significant step forward in addressing AMR. By prioritising WHO access category antibiotics and tailoring treatment to individual susceptibility profiles, the personalised AST approach not only improves the efficiency of the testing process but also supports global efforts to preserve the effectiveness of critical antibiotics.

Click here to read the full paper Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use.