Journal Article

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

Wählby, Carolina, Elf, Johan, Hallström, Erik, Kandavalli, Vinodh, Ranefall, Petter

2023

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Antibiotics / Artificial neural networks / Bacteria / Bacterial diseases / Bacterial Infections / Classification / Computational linguistics / Computer Vision and Robotics (Autonomous Systems) / Computerized Image Analysis / Computerized Image Processing / Datasets / Datoriserad bildbehandling / Deep Learning / Disease susceptibility / E coli / Experiments / Health aspects / Humans / Identification and classification / Language processing / Machine learning / Medical Image Processing / Microfluidic devices / Microfluidics / Microscope and microscopy / Microscopy / Microscopy, Phase-Contrast / Natural language interfaces / Neural networks / Neural Networks, Computer / Performance assessment / Phase contrast / Public health / Species / Species classification / Technology application