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If bacteria become completely resistant to all antibiotics, this treatment will effectively become useless, and simple infections could cause deaths. In fact, this problem is already causing an estimated 1.3 million deaths every year.
One of the biggest challenges is that current testing methods can take up to two days to determine the most effective antibiotic for an infection. The goal of the Oxford Martin Programme on Antimicrobial Resistance Testing was to create a test that detects whether a patient’s bacteria are resistant to antibiotics within an hour. The test works by taking images of a patient’s bacteria under a microscope and using artificial intelligence (AI) to look for any changes that occur when antibiotics are applied to these samples. We recently published results from our citizen science project that investigated what makes some of these bacteria harder for AI to classify.
We collected thousands of images of resistant and sensitive bacteria treated with antibiotics. Bacteria that are sensitive to an antibiotic treatment develop changes to their shape, DNA, and cell wall. The AI model learns to detect these changes by studying images of bacteria that have responded to the antibiotic treatment and images of bacteria that don’t.
Sometimes, even though our AI model has seen thousands of images of antibiotic-treated bacteria, it still makes mistakes. In a recent research paper, we showed that our current model is about 80% accurate at classifying each Escherichia coli (E. coli) cell. Although this leads to very high confidence when determining whether a whole sample is antibiotic sensitive or resistant, we want our diagnostic test to be as robust as possible.
We noticed that there was some variation in the extent to which E. coli cells changed after the antibiotic treatment, even when they were treated with the same concentration of antibiotic and had the same level of antibiotic resistance. In some cases, cells that looked like a resistant cell were actually sensitive, and vice versa. We started the Infection Inspection project to see which bacterial cells were most likely to be misinterpreted by volunteers, so that we could learn what features might also confuse the AI model. Then, we could focus on understanding those types of cells in our future research. We were also curious whether humans could detect more nuanced features than the AI model.
Volunteers were shown a picture of an E. coli cell that we treated with the antibiotic ciprofloxacin, stained, and imaged with our microscope. Because we grew the E. coli from strains collected at the hospital microbiology laboratory, we knew which strains were sensitive or resistant to ciprofloxacin on standard tests. Equipped with the field guide, volunteers could classify each image as resistant, sensitive, or an image processing error.
Read the full story on the Oxford Martin School website.