Microscopes enhanced with artificial intelligence (AI) could help in the quick and accurate diagnosis of the deadly blood infections, which may improve patients’ odds of survival, according to a study.
The bacteria that most often cause bloodstream infections include the rod-shaped bacteria including Escherichia coli or E.coli, the round clusters of Staphylococcus species, and the pairs or chains of Streptococcus species.
Rapid identification and delivery of antibiotic medications is the key to treating bloodstream infections, which can kill up to 40 per cent of patients who develop them.
In the study, led by scientists Beth Israel Deaconess Medical Centre (BIDMC) in Boston, used an automated microscope designed to collect high-resolution image data from microscopic slides.
A convolutional neural network (CNN) — a class of artificial intelligence modelled on the mammalian visual cortex and used to analyse visual data — was trained to categorise bacteria based on their shape and distribution.
To train the AI system, the scientists fed the neural network more than 100,000 images from blood samples.
The machine intelligence learned how to sort the images into the three categories of bacteria — rod-shaped, round clusters, and round chains or pairs — ultimately achieving nearly 95 per cent accuracy, the researchers said.
“This marks the first demonstration of machine learning in the diagnostic area,” said James Kirby, Director at the Clinical Microbiology Laboratory at BIDMC.
“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,” Kirby added, in the paper published in the Journal of Clinical Microbiology.
Automated classification can also ameliorate the shortage of human technologists by helping them work more efficiently, “conceivably reducing technologist read time from minutes to seconds”, Kirby suggested.
In addition, the new tool could also have applications in microbiology training and research.