Researchers supported by the Oxford Martin Programme on Antimicrobial Resistance Testing at the University of Oxford have reported advances towards a novel and rapid antimicrobial susceptibility test that can return results within as little as 30 minutes.
In their study published in Communications Biology, the team used a combination of fluorescence microscopy and Artificial Intelligence (AI) to detect antimicrobial resistance (AMR). This method relies on training deep-learning models to analyse bacterial cell images and detect structural changes that may occur in cells when they are treated with antibiotics. The method was shown to be effective across multiple antibiotics, achieving at least 80% accuracy on a per-cell basis.
The researchers say their model could be used to identify whether cells in clinical samples are resistant to a range of a wide variety of antibiotics in the future.
The deep-learning models were able to detect antibiotic resistance reliably and at least 10 times faster than established state-of-the art clinical methods considered to be gold standard.
Co-author of the paper Achillefs Kapanidis, Professor of Biological Physics and Director of the Oxford Martin Programme on Antimicrobial Resistance Testing, said: “Our AI-based approach detects such changes reliably and rapidly. Equally, if a cell is resistant, the changes we selected are absent, and this forms the basis for detecting antibiotic resistance.”