Antimicrobial resistance continues to pose a major health threat worldwide as more bacteria are either identified that are resistant to one or more antimicrobials or the global spread of previously characterised organisms grows wider. One reason for this spread is due to the overuse of antimicrobials in society.
Prominent examples include MRSA (methicillin-resistant Staphylococcus aureus), VISA (vancomycin-intermediate S. aureus), VRSA (vancomycin-resistant S. aureus), ESBL (Extended spectrum beta-lactamase), VRE (vancomycin-resistant Enterococcus) and MRAB (multidrug-resistant Acinetobacter baumannii).
A new mobile application called Antibiogo, developed and tested by Doctors Without Borders/Médecins Sans Frontières (MSF), is to be used to help facilitate the diagnosis of antibiotic resistance. The device is to roll out to several MSF laboratories, including in Jordan and the Democratic Republic of Congo.
The technology provides a new innovative diagnostic option to help tackle a global health emergency, particularly in low- and middle-income countries. It comes in the form of a smartphone application.
The Antibiogo mobile application was developed by the MSF Foundation, for and with lower income countries. The application uses image processing, artificial intelligence technology, and an existing expert system that includes thousands of interpretation rules from European or American societies for microbiology used to read and interpret antimicrobial susceptibility tests.
It comes in the form of a free, downloadable application. The software is easy to easy for a non-expert laboratory technician to measure and interpret antimicrobial susceptibility tests. These are tests that are used to determine if bacteria will respond to particular antibiotics.
Such tests help doctors prescribe the most effective antibiotics. Conventionally, they require interpretation by highly trained microbiologists. In many cases, LMICs do not have the equipment necessary to carry out such testing and they often lack sufficient numbers of clinical microbiologists. This makes the identification of antibiotic resistance is much more challenging.
The technology enables the user to obtain the resistance profile of the bacteria responsible for a patient infection.
