Infection Inspection

Help this global project to develop a faster test for antibiotic resistance by looking inside bacteria that have been treated with antibiotics. This will improve healthcare for patients with infections.

URL: www.zooniverse.org/projects/conor-feehily/infection-inspection

The Infection Inspection team is studying bacteria treated with antibiotics to learn what antibiotic-sensitive and antibiotic-resistant bacteria look like under a microscope. They want to use this information to create a rapid test that healthcare professionals could use to find out what bacteria is causing a patient’s infection and which antibiotics could be used to treat them.

With your help, we may be able to reduce the time it takes to treat bacterial infections.

Infection Inspection researcher

Reach: Worldwide

Nature of science focus: Online citizen science (OCS) projects can be used to develop any of the Nature of Science (NoS) substrands. Identify aspects of NoS that your students need to get better at or understand more fully and then frame your unit to be very clear about these things when you do them.

Science capability focus: Gather and interpret data, Interpret representations, Engage with science

Science focus: Human biology, disease, medical research

Some suggested science concepts:

• Bacteria can be both good and bad – for example, scientists use E. coli as an important laboratory tool.
• Cells are found in every living organism.
• The body has an immune response to infection caused by bacteria.
• The classification of bacteria.
• Mutation and life cycles of microorganisms.
• How are antibiotics produced?
• How does antibiotic resistance develop?

Many concepts could be learned – focusing on a few can often be more powerful. Develop your learning outcomes and success criteria from these concepts as well as the Nature of Science strand and the science capabilities.

Some examples of learning outcomes:

Students can:

• develop their observation skills by learning to identify the changes in the bacteria
• use observation skills to identify features for classification
• explore the impact on human health and life expectancy if antibiotics were to stop working
• learn what they can do to help reduce the spread of antibiotic resistance
• research why it is important to get speedy and accurate diagnoses
• explain why the reliability of results is important and how scientists know that citizens’ help in analysis is reliable
• describe the limitations and advantages of using machine learning for medical diagnosis
• understand that engaging with science via a citizen science project can make a difference to a global issue.

Antibiotic resistance

Antibiotics have saved lives for decades now – without antibiotics, common infections, surgery, childbirth and cancer treatments would be much more dangerous. Antibiotic Research UK estimates that antibiotics have increased the average life expectancy in the developed world by as much as 20 years. Unfortunately, antibiotic resistance around the world is increasing and we have seen outbreaks of so-called superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). The Infection Inspection project is concerned that globally antibiotic-resistant bacterial infections cause about 700,000 deaths per year worldwide, and if we do nothing, this may rise to 10 million deaths per year by 2050.

About Infection Inspection

To help identify if a patient’s infection is caused by a bacteria resistant to antibiotics, we need new tests that can provide quick results so the best medication can be given. To help solve this issue, the Infection Inspection team is developing a test that can detect the infectious bacteria, identify the species and determine whether it is sensitive to commonly used antibiotics. A sample will be filtered, imaged with a microscope and then analysed by a computer to give results within an hour. Currently, tests can take a day or more to complete, leading to delays in treatment.

They need citizen scientists to help the team learn what antibiotic-resistant bacteria look like under their microscope. They have thousands of images that need to be reviewed of resistant and sensitive bacteria that have been treated with antibiotics. Bacteria that are sensitive to an antibiotic treatment develop changes to their shape, DNA and cell wall. This is because the antibiotic interferes with their life functions. The Infection Inspection team has built a machine learning tool that can recognise these changes in sensitive bacteria, but it needs human-annotated data to help it better understand the features of resistant bacteria, especially in cases when the computer and human predictions differ. The participation of citizen scientists from around the world will help the machine learning tool understand what makes a bacterium ‘confusing’ to classify and so help to improve the accuracy of the test.

Taking part

Images of Escherichia coli (E. coli) taken on a laboratory microscope 30 minutes after an antibiotic treatment are displayed on the website. Users are asked to see if they think the bacteria image displayed is resistant or sensitive to the antibiotic ciprofloxacin.

Under the Classify tab, before you start the first task, follow the tutorial for detailed instructions. This will explain how to identify a bacterium when it is resistant or sensitive to ciprofloxacin. There is also the option to select ‘Image processing error’ if the image is blurry or looks wrong. If you have more queries, go to the Talk tab – you might find your question has already been answered or you can ask questions and the researchers will reply.

Find out more about the team behind Infection Inspection here – it includes microbiologists, medical doctors, biophysicists and machine learning experts. The project is funded by the Oxford Martin Programme on Antimicrobial Resistance Testing.