Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification.

ABSTRACT: For effective treatment of bacterial infections, it is essential to identify the species causing the infection as early as possible. Current methods typically require hours of overnight culturing of a bacterial sample and a larger quantity of cells to function effectively. This study uses one-hour phase-contrast time-lapses of single-cell bacterial growth collected from microfluidic chip traps, also known as a "mother machine". These time-lapses are then used to train deep artificial neural networks (Convolutional Neural Networks and Vision Transformers) to identify the species. We have previously demonstrated this approach on four different species, which is now extended to seven common pathogens causing human infections: Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Ac

SUBMITTER: Hallstrom E 

PROVIDER: S-EPMC12416834 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature