Deep Learning and Single Cell Phenotyping for Rapid Antimicrobial Susceptibility Testing
The Oxford Martin Programme on
Antimicrobial Resistance Testing
During the past 80 years, the scientific community has managed to control many bacterial infections using antibiotics. However, the widespread use of antibiotics has led to the emergence of strains of antibiotic-resistant bacteria, causing at least 700,000 deaths per year worldwide. If left unchecked, this figure will rise to 10 million by 2050, threatening to take humanity back to the pre-antibiotic era.
Current tests to determine whether bacteria are resistant to antibiotics usually take a day to complete, leaving hospital clinicians no option but to treat urgent cases of severe infection with broad spectrum antibiotics, compounding the problem of resistance. Increasing the speed at which analysis takes place is key to tackling antibiotic resistance: our aim is to revolutionise diagnosis by reducing its timescale from 12-24 hours to less than an hour.
This programme aims to develop rapid tests that can both identify bacterial species and establish which antibiotics they are susceptible to, in as little as 30 minutes. Part of the problem at present is that bacteria must be isolated and cultured within labs, leading to bottlenecks in testing.
We intend to enable direct testing of clinical samples, using ultrasensitive microscopy tests, sophisticated image analysis and machine learning, hugely speeding up the process by which clinicians obtain the information they need. It will enable precise and personalised treatment of infections with narrow spectrum antibiotics, reducing the unwarranted use of broad-spectrum antibiotics.
featured video and long read
The Oxford Martin Programme on Antimicrobial Resistance Testing is developing a new type of medical test powered by AI that is portable and accessible anywhere, from a hospital to a pharmacy to a field tent serving victims of a natural disaster. The aim is to be able to determine the cause of an infection and how best to treat it, all within 30 minutes.Find out more in our long read
Clinical Infection Consultant
Professor of Microbiology
Professor of Biological Physics
MRC Methodology Research Fellow
Consultant in Infection