Using AI 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.

Howard A, Hughes DM, Green PL, Velluva A, Gerada A, Maskell S, Buchan IE, Hope W.
Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use.
Nat Commun. 2024 Nov 21;15(1):9924. doi: 10.1038/s41467-024-54192-3

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...