Infection Inspection: Using the power of citizen science to help with image-based prediction of antibiotic resistance in Escherichia coli

Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its escalating threat. This study introduces innovative approaches for expedited bacterial antimicrobial resistance profiling, addressing the critical need for swift clinical responses. Between February and April 2023, we conducted the Infection Inspection project, a citizen science initiative in which the public could participate in advancing an antimicrobial susceptibility testing method based on single-cell images of cellular phenotypes in response to ciprofloxacin exposure. A total of 5,273 users participated, classifying 1,045,199 images. Notably, aggregated user accuracy in image classification reached 66.8%, lower than our deep learning models performance at 75.3%, but accuracy increased for both users and the model when ciprofloxacin treatment was greater than a strains own minimum inhibitory concentration. We used the users classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications were due to cellular features that varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our deep learning-based bacterial classifier and diagnostic methodology. Our successful engagement with the public through citizen science is another demonstration of the potential for collaborative efforts in scientific research, specifically increasing public awareness and advocacy on the pressing issue of antibiotic resistance, and empowering individuals to actively contribute to the development of novel diagnostics. Lay summaryAntibiotic resistance is a big health problem worldwide. We need fast ways to find out if bacteria are resistant to antibiotics. In our study, we develop new methods to do this quickly. We ran an online project called Infection Inspection from February to April 2023, in which 5,273 people took part. Together, they classified more than a million pictures of bacterial cells, helping our project use these pictures to detect antibiotic resistance. The volunteers performed well, getting near 67% of the answers right. We also learned which pictures helped or confused them. This will help us make our computer program better. This project didnt just help science; it also taught people about antibiotic resistance. Partnerships between the public and scientists can make a difference to developing technologies that protect our health.