from the bug-off dept.
Microscopes enhanced with artificial intelligence (AI) could help clinical microbiologists diagnose potentially deadly blood infections and improve patients' odds of survival, according to microbiologists at Beth Israel Deaconess Medical Center (BIDMC). In a paper published in the Journal of Clinical Microbiology, the scientists demonstrated that an automated AI-enhanced microscope system is "highly adept" at identifying images of bacteria quickly and accurately. The automated system could help alleviate the current lack of highly trained microbiologists, expected to worsen as 20 percent of technologists reach retirement age in the next five years.
"This marks the first demonstration of machine learning in the diagnostic area," said senior author James Kirby, MD, 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."
[...] While human technologists routinely provide highly accurate diagnoses, demand for these highly skilled workers exceeds supply in the United States. Nine percent of lab technologists remain unfilled, and that number is expected to dramatically increase as technologists of the Baby Boomer generation begin to retire in droves, according to a 2014 survey from the American Society for Clinical Pathology.
What's more, these images can be sent remotely, bringing the highest level expertise anywhere the internet reaches. That's critical, as rapid identification and delivery of antibiotic medications is the key to treating bloodstream infections, which can kill up to 40 percent of patients who develop them. Each day a patient goes untreated is linked with an increased risk of mortality.
In addition to its clinical uses, the new tool could also have applications in microbiology training and research, Kirby noted.
"The tool becomes a living data repository as we use it," he said. "And could be used to train new staff and ensure competency. It can provide unprecedented level of detail as a research tool."
Kenneth P. Smith, Anthony D. Kang, James E. Kirby. Automated Interpretation of Blood Culture Gram Stains using a Deep Convolutional Neural Network. Journal of Clinical Microbiology, 2017; JCM.01521-17 DOI: 10.1128/JCM.01521-17