Microscopes enhanced with artificial intelligence (AI) could help alleviate the current lack of highly trained clinical microbiologists that is expected to worsen as 20% of technologists reach retirement age in the next five years.
According to a paper published last month in the Journal of Clinical Microbiology bymicrobiologists at Beth Israel Deaconess Medical Center (BIDMC), their automated AI-enhanced microscope system is "highly adept" at identifying images of bacteria quickly and accurately.
"This marks the first demonstration of machine learning in the diagnostic area," said senior author 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."
Kirby's team used an automated microscope designed to collect high-resolution image data from microscopic slides. In this case, blood samples taken from patients with suspected bloodstream infections were incubated to increase bacterial numbers. Then, slides were prepared by placing a drop of blood on a glass slide and stained with dye to make the bacterial cell structures more visible.
Next, they trained a convolutional neural network to categorize bacteria based on their shape and distribution. These characteristics were selected to represent bacteria that most often cause bloodstream infections; the rod-shaped bacteria including E. coli; the round clusters of Staphylococcus species; and the pairs or chains of Streptococcus species.
"Like a child, the system needed training," said Kirby. "Learning to recognize bacteria required a lot of practice, making mistakes and learning from those errors."
To train it, the scientists fed their neural network more than 25,000 images from blood samples prepared during routine clinical workups. By cropping these images—in which the bacteria had already been identified by human clinical microbiologists—the researchers generated more than 100,000 training images. 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% accuracy.
Next, the team challenged the algorithm to sort new images from 189 slides without human intervention. Overall, the algorithm achieved more than 93% accuracy in all three categories. With further development and training, Kirby and colleagues suggest the AI-enhanced platform could be used as fully automated classification system in the future.
In the meantime, Kirby suggests automated classification can ameliorate the shortage of human technologists by helping them work more efficiently, "conceivably reducing technologist read time from minutes to seconds," he said.