Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: J.E. has founded and is partly engaged in the AMR diagnostics company Sysmex Astrego AB, https://www.sysmex-astrego.se. This competing interest will not alter adherence to PLOS policies on sharing data and materials.
(Copyright: © 2023 Hallström et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)