Co-author Dr Piers Turner, Oxford Martin Programme on Antimicrobial Resistance Testing and Department of Physics, University of OxfordIn their study published in Communications Biology, the team used a combination of fluorescence microscopy and artificial intelligence (AI) to detect antimicrobial resistance (AMR). 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. According to the Global Research on Antimicrobial Resistance (GRAM) Project – a partnership involving the University – almost 1.3 million people died in 2019 due to AMR. Current testing methods for antibiotic susceptibility rely on growing bacterial colonies in the presence of antibiotics – typically requiring several days. You can learn more about how the Oxford Martin Programme on Antimicrobial Resistance Testing is developing a rapid, AI-powered test for AMR here.