Rapid identification of individual bacterial pathogens using three-dimensional quantitative phase imaging and artificial neural network (Conference Presentation)

Abstract

Rapid identification of infectious pathogens can save lives and mitigate healthcare expenses. Yet the current turnaround time for microbial identification typically exceeds 24 hours, as the common methods require the cultivation of millions or more bacteria to detect the collective signal. In this study, we propose a hybrid framework of quantitative phase imaging and artificial neural network to facilitate rapid identification at an individual-cell level. Specifically, three-dimensional images of refractive index were acquired for individual bacteria, and an optimized artificial neural network determined the species based on the three-dimensional morphologies, securing 82.5% blind test accuracy at an individual-cell level.