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You are here: Home > Modernising Medical Microbiology and Big Infection Diagnostics > Citizen scientists invited to help tackle antibiotic resistance

Citizen scientists invited to help tackle antibiotic resistance

8 February 2023 · Listed under Modernising Medical Microbiology and Big Infection Diagnostics

A research team in Oxford has launched a new project involving members of the public that is aimed at developing diagnostics for infection and antibiotic resistance.

Infection Inspection logo and microscope images of resistant E.coli and sensitive E.coli

The team of physicists, microbiologists, data scientists and doctors, who are supported by the NIHR Oxford Biomedical Research Centre (BRC), have launched the ‘Infection Inspection’ citizen science project, hosted on the Zooniverse platform.

Using a similar model to the successful ‘Bash the Bug’ project, ‘Infection Inspection’ asks members of the public to help classify images of E. coli that are either resistant or sensitive to an antibiotic.

These volunteers will look at images of bacteria captured by the team on microscopes to determine if the samples are resistant or sensitive to an antibiotic.

Dr Conor Feehily. of the Modernising Medical Microbiology group at the Nuffield Department of Medicine and of the Oxford Martin School Programme on Antimicrobial Resistance, said: “The number of bacteria that are resistant to antibiotics is increasing, which creates a big problem. By developing much faster tests for antibiotic resistance, we hope we can help clinicians select the best antibiotic to use much faster.

The technique used is based on the changes in a bacterium’s cell structure that can be seen under a high sensitivity microscope when it is exposed to antibiotics. Depending on the resistance of the bacteria, different responses are observed.

“We are asking members of the public to look at pictures of bacteria and tell us if the changes indicate antibiotic resistance or sensitivity,” Dr Feehily explained. “The images were collected as part of our research to develop a test for antibiotic resistance that uses machine learning to provide rapid and accurate results. We will use Infection Inspection to improve our models and provide a reference to how well the computer model can predict resistance, compared to lots of human eyes.

The project is funded by the Oxford Martin Programme on Antimicrobial Resistance Testing. If you would like to get involved and give it a try, visit: https://www.zooniverse.org/projects/conor-feehily/infection-inspection”

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