Automated Artificial Intelligence Speeds Identification of Blood Pathogens GEN

Scientists in Boston have developed an automated artificial intelligence (AI)-guided microscopy system that can help diagnose serious bloodstream infections (BSIs) quickly and accurately. The technology, which uses a trained convolutional neural network (CNN) to recognize the different shapes and distribution of pathogenic bacteria, could help to speed diagnosis and potentially save patient lives, as well as address the current lack of trained microbiology technologists, suggest its developers at Harvard Medical School and Beth Israel Deaconess Medical Center (BIDMC). "This marks the first demonstration of machine learning in the diagnostic area," comments James Kirby, M.D., director of the Clinical Microbiology Laboratory at BIDMC, and associate professor of pathology at Harvard Medical School. "With further development, we believe this technology could form the basis of a future diagnostic platform that augments the capabilities of clinical laboratories, ultimately speeding the delivery of patient care." The researchers report on the technology in the Journal of Clinical Investigation, in a paper entitled "Automated Interpretation of Blood Culture Gram Stains using a Deep Convolutional Neural Network."