Using Artificial Intelligence to Detect Antibiotic Resistance

Susceptible or resistant? When it comes to treating bacterial infections, the answer is crucial. In the clinic, microbiologists test bacterial isolates from patients to determine whether they are susceptible or resistant to a given antibiotic. The downside: clinical testing methods can take up to 2 days, during which patients may be treated with suboptimal antibiotics. But artificial intelligence could help. Scientists are finding ways to use Ai and machine learning to develop testing tactics that are easy, accurate—and quick. 📝 Microbial Minutes Audience Survey: asm.org/mmsurvey Featured Study Zagajewski A., et al. Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli. Communications Biology, Nov. 14, 2023. https://www.nature.com/articles/s42003-023-05524-4 Additional Sources Andersson D., et al. Mechanisms and clinical relevance of bacterial heteroresistance. Nature Reviews Microbiology, June 24, 2019. https://www.nature.com/articles/s41579-019-0218-1 Bhattacharyya R.P., et al. Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination. Nature Medicine, Nov. 25, 2019. https://www.nature.com/articles/s41591-019-0650-9 Ding Y., et al. Artificial intelligence-assisted point-of-care testing system for ultrafast and quantitative detection of drug-resistant bacteria. SmartMat, May 10, 2023. https://onlinelibrary.wiley.com/doi/full/10.1002/smm2.1214 Hu X., et al. Novel Clinical mNGS-Based Machine Learning Model for Rapid Antimicrobial Susceptibility Testing of Acinetobacter baumannii. Journal of Clinical Microbiology, April 6, 2023. https://journals.asm.org/doi/10.1128/jcm.01805-22 Maheshwari R. What is Deep Learning AI? Forbes Advisor, April 3, 2023. https://www.forbes.com/advisor/in/business/software/what-is-deep-learning-ai/ Pascucci M., et al. AI-based mobile application to fight antibiotic resistance. Nature Communications, Feb. 19, 2021. https://www.nature.com/articles/s41467-021-21187-3 Prinzi, A. Updating Breakpoints in Antimicrobial Susceptibility Testing. Asm.org, Dec. 13, 2023. https://asm.org/Articles/2022/February/Updating-Breakpoints-in-Antimicrobial-Susceptibili Stokes J.M., et al. A Deep learning Approach o Antibiotic Discovery. Cell, Feb. 20, 2020. https://doi.org/10.1016/j.cell.2020.01.021 Wang, S., et al. A Practical Approach for Predicting Antimicrobial Phenotype Resistance in Staphylococcus aureus Through Machine Learning Analysis of Genome Data. Frontiers in Microbiology, March 2, 2022. https://www.frontiersin.org/articles/10.3389/fmicb.2022.841289/full Wong F., et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature, Dec. 20, 2023. https://www.nature.com/articles/s41586-023-06887-8 Vasala A., et al. Modern Tools for Rapid Diagnostics of Antimicrobial Resistance. Frontiers in Cellular and Infection Microbiology. July 15, 2020. https://doi.org/10.3389%2Ffcimb.2020.00308 👍 Subscribe to ASM's YouTube channel at https://goo.gl/mOVHlK ✅ Become a member today at https://asm.org/Membership 🔬 Learn more about the American Society for Microbiology at https://www.asm.org/ 📱 Join us on social: Facebook: https://www.facebook.com/asmfan Twitter: https://www.twitter.com/ASMicrobiology Instagram: https://www.instagram.com/asmicrobiology/

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